\r\n\tIn this book, the authors will present the highlights of basic research of biomechanical and biochemical pathways of bone homeostasis and the developing clinical methods for treatment of bone loss, either following trauma or systemic disease.
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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"59369",title:"Exploring the Pedagogy of Online Feedback in Supporting Distance Learners",doi:"10.5772/intechopen.74028",slug:"exploring-the-pedagogy-of-online-feedback-in-supporting-distance-learners",body:'\n
\n
1. Introduction
\n
Giving and receiving feedback represent a type of dialog between educators and learners that supports learners in modifying their academic performance going forward. As such, the value of this pedagogical practice is well recognized in the literature (for example, see [1, 2, 3, 4, 5, 6]). As a formative process, feedback does not attempt to formally evaluate the standard of work, rather it is designed to point students in the right direction through commenting, questioning, scaffolding, reminding, and offering models and examples. However, in contrast to the more summative process of formal assessment, it is claimed [7] that theory of feedback, in general, is lacking; moreover, research into online feedback is still in its infancy (for example, see [8, 9, 10, 11, 12, 13]). However, it is more important than ever to research this area, as increasing numbers of students enroll in online courses [14] and, with this increase, faculty are required to spend more time responding to distance learners and adopting new skills and practices [15, 16, 17, 18]. Indeed, the literature shows that for many faculty, the transition from providing immediate verbal feedback in a classroom to delayed written feedback in an online forum is not a seamless one [19]. This is compounded when institutional service goals specifying the roles and responsibilities of the instructor are not explicit [20]. Overcoming these challenges is critical, since it is shown that effective feedback is not only linked to higher university ratings [13], but more specifically, it is linked to student satisfaction with online learning [4, 9, 21, 22, 23, 24]. Considering the attrition rate of online and distance learning students is considerably higher than on-site courses [12, 25], the need to improve the quality on online feedback cannot be underestimated. Thus, the aim of this paper is to explore the interrelational nature of online feedback by examining what and how feedback was given to distance learners by their instructor and how it was perceived by the students. This dual perspective attempts to redress traditional hierarchies by making the ‘evaluator’ the ‘evaluated’ and, in so doing, offer insights into established feedback practices and suggestions for how they might be improved.
\n
\n
\n
2. Background
\n
\n
2.1. Terminology
\n
At its most general level, feedback can be defined as the means by which learners are able to determine their progress towards an end goal [6]. However, there appears no widely accepted definition [5] with most definitions reflecting various cognitive, social, and affective perspectives. At a cognitive level, feedback has been defined as a means of controlling learning through the use of reinforcement, i.e. punishment or reward [26]. Such a behaviorist approach has been replaced by more constructivist thinking in which ‘knowledge of results’ (p. 310) emerges from an ongoing process of forming and testing hypotheses rather than a memorization of results [27]. Added to this is the recognition of social interaction in the formation of feedback as instructors respond to learners and check if the information is appropriate to a given task [28, 29]. The affective features of feedback are also recognized with feedback defined as any message which praises, encourages, and supports learners in reaching a specific learning goal [30]. It is also noteworthy that the term “feedback” has often been used synonymously with “formative assessment” and “formative feedback” to imply information about a student’s progress as distinct from formal results-based summative assessment [4]. Within the present study, online feedback is used to denote any messages intended to support students so that they can monitor their own performance in terms of learning goals and strategies. It also refers to any collective, personalized, detailed, written, and/or oral messages provided to distance learners in order to guide and support their learning.
\n
\n
\n
2.2. Feedback types
\n
The two most rudimentary types of feedback are “verification,” an evaluation of the learner’s work, and “elaboration,” the guided instructional cues given to direct the learner [31]. Additionally, feedback can be “norm-referenced” comparing the learner’s answers to others, as in comments such as “this is below average,” or it may be “criterion-referenced,” indicating what has been done and how the answer might be improved [32]. Other feedback typologies include “corrective,” “informative,” and “Socratic,” of which the latter involves asking questions to direct the learner [33]. Yet, other models identify feedback along a continuum ranging from “no feedback,” “error-flagging,” involving highlighting errors without correction and “informative tutoring,” involving providing elaborate feedback with strategies for revision [34].
\n
\n
\n
2.3. Delivering online feedback
\n
Apart from being online, there are several other considerations to take into account when delivering online feedback. In one study [35], weekly podcasts delivered to a whole class were rated positively by learners who also used the additional examples and advice as a supplementary resource for revision. This method was also perceived positively by instructors as a time-efficient way to provide feedback to all students at one time. However, this one-way method of communication fails to address individual learning needs, and other research [9] indicates that students who receive personalized feedback have higher levels of course satisfaction and perform academically better that those students who receive only collective feedback.
\n
In addition, another issue regarding delivery of online feedback focuses on who initiates the feedback. One study [36] suggests that in most cases, learners are unwilling to participate in giving feedback to their peers, and that the reason most students give for logging-on is to read the contributions of other students and any accompanying feedback from the instructor. Even when learners do offer feedback, it tends to be in the form of “verification”, as in “I agree” rather than the more elaborative feedback offered by lectures regarding learning content, task, and social participation [37]. There is also evidence about online learning preferences that suggests that some students do not rate social interaction with peers as important or contributing to their learning [38].
\n
This is not to say that learner engagement in the feedback process cannot be increased. One study [8] showed how teaching explicit metacognitive strategies such as offering support, eliciting information, asking questions, establishing a situation, offering a possible solution, etc., as well as monitoring and evaluating student participation, all have a positive impact on the feedback process. It is also shown that student interaction and feedback are also enhanced when participating in online discussion forums becomes mandatory [36, 39].
\n
\n
\n
2.4. Characteristics of effective online feedback
\n
What makes online feedback effective is determined by both educators and learners. According to educators, effective online feedback is time-consuming requiring detailed and “substantive” comments rather than “basic” ones such as “good answer” [40, 41]. It should promote higher order thinking skills and ask learners to reflect on their learning by asking questions which require learners to clarify, summarize, hypothesize, and link ideas to other areas of course content [42]. Ongoing analysis of online dialog suggests that effective online feedback requires instructor and students to play equal roles in the “assessment of process” [39]. Moreover, educators recognize that in the design of Web-based learning tools, specific principles are more effective, e.g., feedback should summarize learners’ performance, motivate, be relevant to assessment criteria, and be manageable and timely [11]. Added to this, it is recognized that effective online feedback should be clear and comprehensible, leaving no room for confusion or doubt [43].
\n
Such characteristics of best practice are hardly contentious; however, research suggests that these criteria can be perceived differently by learners than by instructors. For instance, in one study [44] on feedback in general, instructors consider their feedback to be more useful than their students; they also believe their students are less interested in feedback than final grades; they also perceive their grading to be fairer in contrast to students who perceive bias toward more active class members. However, both students and instructors acknowledge difficulties for students in decoding feedback, and both groups acknowledge the emotional impact of feedback on student motivation. Similarly, in another study of feedback [13], feedback is linked to students’ individual learning styles with students identified as “deep” learners preferring feedback that encourages reflection, and students identified as “surface” learners preferring positive feedback that verifies an answer and does not require further participation [13]. Another study [23], comparing student satisfaction with distance learning and on-site learning, shows that distance learning students are less satisfied with feedback in relation to comprehensibility, emotional impact, and fairness. It is also been shown that similar to educators [39], online graduate students perceive effective feedback as mutually constructed with their instructors, and that it should also allow space for students to negotiate learning goals and mutually agreeable deadlines [10].
\n
\n
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2.5. Challenges and concerns
\n
The literature also highlights several areas of concern and challenge. Conceptually, feedback is considered an under-theorized area that focuses on informal and dialogic processes that are difficult to measure [7]. Moreover, it is claimed that the turn to constructivist pedagogy remains a challenge for online education, in general, and feedback, in particular [45]. On a practical level, multiple taxonomies and models of feedback indicate that it is generally not a well-understood area of online teaching, and, as such, it may possibly be delivered ineffectively, e.g., too little, too late, incomprehensible, demotivating, etc. [46]. This is supported by research which suggests that students cite feedback containing complaint or dissatisfaction as reasons for low participation [47]. This too is in line with research that indicates that instructors new to online teaching often underestimate the need for consistent support and presence in encouraging, motivating, and keeping students focused [20]. A final concern relates the contextual setting that either supports or discourages effective online teaching. Factors such as institutional recognition, promotion, compensation, technical support, and overall work-load are shown to directly influence the quality and quantity of faculty participation [48]. Overall, the extant literature highlights the complexities of the topic and the related challenges involved in delivering feedback in an online setting.
\n
\n
\n
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3. Methodology
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3.1. Purposes of study and research questions
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The study was based on data collected over the course of one academic year (2015/2016). This descriptive study uses a mixed method approach to describe educational phenomenon, as it exists and compares it to what is desired [49]. Such research allows for description, explanation, and improvement of educational practices and does not attempt to draw conclusions based on cause and effect. Rather, the overall aim of this study is to uncover data that may not have previously been encountered using other research approaches. Thus, the research questions guiding this study are (1) what feedback was given to distance learning students?, and (2) how was it perceived by these students?
\n
Emerging from these questions, the use of a mixed methods approach allowed for creative data collection, the collection of thick, rich data [50], and the possibility of uncovering contradictions [51]. Indeed, the use of content analysis in phase 1 of the study (see Section 3.2.1) allowed for a systematic and descriptive investigation of the types and frequency of feedback offered by the lecturer to her DL students. The coding of the textual features of this feedback provided inferences about the types of feedback provided, to whom and how often. Moreover, in phase 2 of the study (see Section 3.2.2), semistructured interviews provided opportunities for the researcher/instructor to uncover students’ beliefs, attitudes, and perceptions about the feedback they received, as well as follow themes and issues that may not have been previously considered [52].
\n
\n
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3.2. Context
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This study took place at a higher education institution in Cyprus, where degree programs are offered in a variety of modes, face-to-face, online, and distance learning courses, for undergraduate and graduate students. While online courses are an option for face-to-face students, distance learning students are geographically distant from the institution with most of them resident in other European countries. Most instructors in this institution have recently added online and distance learning courses to their teaching loads and, as such, are responsible for the design, management, and delivery of these courses. Against this background, the researcher/instructor sets out to explore the feedback given to distance learning (DL) students studying for a postgraduate degree Teaching English to Speakers of Other Languages (TESOL). The degree offers a combination of nine courses, each of which is delivered to students over the period of a 12 week semester. Course content consists of weekly prerecorded video lectures, audio presentations, and recommended readings and students are required to reflect on and discuss this material, critique ideas, ask questions, express opinions, and collaborate on individual and group tasks such as creating lessons and activities. Feedback is then provided in response to these tasks and any other specific academic, administrative, and technical issues that might arise.
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3.3. Participants
\n
At the time of the study, the researcher/instructor had more than 26 years teaching experience, of which only 2 years included teaching DL courses. Data were also collected from interviews with five DL students after completion of the course. The students, four female and one male, were all aged between 24 and 45 years. They were all English language teachers with teaching experience ranging from 5 and 22 years. They were based in different three European countries: Greece, Cyprus, and Germany. Before agreeing to participate in the study, all participants signed a consent form expressing their willingness to participate.
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3.4. Procedures
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3.4.1. Phase 1
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After completion of the course, all messages written and posted to distance learning students over the duration of one semester (Fall 2015) were collected from course forums. The total data set comprises 93 messages posted by the course leader in response to students’ questions, online tasks, and discussions. Content analysis was then conducted based on an established taxonomy of feedback: corrective, informative, and Socratic feedback [33]. The analytic tool was adapted to include corrective, affective, informative, and reflective feedback (Table 1).
\n
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Category
\n
Indicator
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Definition
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Example
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Affective
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Salutations, phatics
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Greetings & expressions purely for social purposes
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Dear students, good morning wishing you all a great week
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\n
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Vocatives
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Addressing participants by name
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Thanks [student name]
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\n
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Complimenting, expressing appreciation
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Complimenting contents of messages
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I really enjoy reading your thoughts and ideas about …
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\n
\n
Empathetic
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Shows empathy
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As we draw towards Christmas, there are more and more things to do and I understand that it is difficult to find time
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\n
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Supportive
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Offers support
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Good luck and please get in touch with me immediately if you have any questions.
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Self-disclosure
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Reveals details of life outside the course
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I am from …and I have been teaching for …
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\n
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Corrective
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Knowledge-of-response (with or without elaboration)
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Positively or negatively evaluates the content of a student’s answer
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You have encapsulated Borg’s ideas very nicely Great answer
\n
\n
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Informative
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Content
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Comments on the content of the course and may include references to theory
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Savignon defines this as communicative competence. Humanist theories give prominence to the affective (emotional) aspects of language learning
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\n
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Procedural
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Gives procedural details of the course (technical, dates, assignments) and directs the learners to do something specific
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Please use this forum to ask questions, make comments and answer questions from your colleagues You can find the book at www.library…
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\n
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Reflective
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Direct and indirect questions
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Asks students to reflect on their understanding of various elements of the course and relates their learning to their professional experience
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Do any of these ideas resonate with your own experience of language teaching? Could it be used effectively in combination with other methods— what do you think?
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\n\n
Table 1.
Analytic model of online teacher feedback.
\n
The software package, NVivo, was used to highlight coded segments of text and produce summaries of findings. Because of the elaborate nature of feedback, single messages may contain multiple codes, so to ensure reliability, an independent rater also coded 10% of the data. A Cohen’s Kappa established interrater reliability as 0.6, which indicates a good possibility of agreement occurring other than by chance. In addition, the percentage of agreement between the two raters was shown to be 91.63%.
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3.4.2. Phase 2
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In the following semester (Spring 2016), semi-structured interviews lasting between 60 and 80 min were conducted with five students who had completed the course the previous semester. All interviews were recorded, and full orthographic and verbatim transcripts were produced. NVivo was used again to identify and code segments of text. Themes and subthemes were then identified and aggregated into hierarchies. This phase of analysis focused on identifying “thick rich descriptions” rather than descriptive statistics.
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4. Findings
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The following sections consider what feedback was given by the researcher/instructor and how it was perceived by the distance learning students.
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4.1. Feedback given
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Findings show that the researcher/instructor posted 93 messages in response to 108 messages posted by students, which equates to a response rate of 86.1%. Of these 93 messages posted by the course leader, 27 messages (29%) were addressed to the whole group as collective feedback, while 66 messages (71%) were addressed to individual students. The quantity of feedback given by the course leader amounted to 10.150 words, which is an average of 109 words per message. Moreover, the response time between students posting their messages and the course leader responding ranged from 0 to 15 days with a mean average response time of 1.7 days. So what type of feedback was given by the course leader (Table 2)? Content analysis shows that the most frequent type of feedback given was informative feedback that focused on content (23%), followed by procedural content addressing administrative and technical details related to the course (17%). Affective feedback dealing with the social, emotional, and motivational aspects of learning was the next most common type of feedback (11%) followed by corrective feedback (7%). The least common feedback was reflective feedback (asking questions that encourage students to reflect on their answers) (4%).
\n
\n
\n
\n
\n\n
\n
Node
\n
Density by % coverage
\n
Density by word number
\n
\n\n\n
\n
Informative-content
\n
23.25
\n
2347
\n
\n
\n
Informative-procedural
\n
16.62
\n
1804
\n
\n
\n
Affective
\n
11.21
\n
1248
\n
\n
\n
Corrective
\n
07.28
\n
737
\n
\n
\n
Reflective
\n
04.01
\n
425
\n
\n\n
Table 2.
Content analysis of feedback.
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4.2. Feedback received
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Findings show that distance learning students’ perceptions of feedback revolve around three main themes: the pedagogical, contextual, and relational dimensions of their learning.
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4.2.1. Pedagogical dimensions
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In relation to the pedagogical dimensions of learning, students identified three subthemes. The first subtheme highlights the positive aspects of feedback as being accessible to all, focused, motivating, personalized, and timely (Table 3).
\n
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Theme
\n
Subtheme
\n
Student comments: examples
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\n\n\n
\n
Positive
\n
Accessible
\n
T: before the exams and during the study period I am trying to read all the messages because I want to see if I am on the right path or not
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Focused
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M: I liked the fact that she always highlighted the part where she had to make comments and she explained very clearly what she expected of me in this particular part
\n
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Motivating
\n
A: to me, feedback is the motivation, I get really motivated even with bad feedback, you know what I mean by bad…it helps me understand what I did and what I was supposed to do, it’s like a clear guidance… I can closely relate it to achievement, yes it also adds to confidence obviously
\n
\n
\n
Personalized
\n
S: but for me you know, maybe this is because I don’t have the face-to-face opportunity… but for me it’s very important for you to know who I am… you know when I write (student’s name) you know it’s me (student’s name) talking
\n
\n
\n
Timely
\n
C: I think a couple of days is reasonable because when I ask something I need to have the answer to that in order to move on with my studying
\n
\n
\n
Useful
\n
T: of course and since I have started this master’s programme I now give my own students the same opportunity to contact me every Saturday at specific hours on the phone, in email or Viber messages to ask and talk about any thoughts they have…
\n
\n\n
Table 3.
Pedagogical dimensions of feedback: positive features.
\n
Conversely, DL students also identified the negative aspects of feedback on their learning as the following: being critical in tone, inconsistent, inconvenient, lacking, and vague and confusing (Table 4).
\n
\n
\n
\n
\n\n
\n
Theme
\n
Subthemes
\n
Student comments: examples
\n
\n\n\n
\n
Negative
\n
Critical
\n
T: for instance, when I ask about references, how to cite something, I am asking because I really don’t know, I have a slight idea but I am not sure if it’s the correct way…so an answer like “you have to do it like masters students do” is not the kind of answer I have been waiting for…
\n
\n
\n
Inconsistent
\n
A: the more the students, the less the feedback or the lateness of the feedback but no this has not proven so, like for example as I told you previously in the independent course there were 2 people and I didn’t get any feedback, either in the forums or the assignments, just nothing, whereas in other courses I got feedback right away and quite detailed…
\n
\n
\n
Inconvenient
\n
A: all WebEx sessions are at times that I have work so I have never been to a WebEx session, so this is my first one actually, so come on guys please talk to us
\n
\n
\n
Lack of
\n
M: but well I kept posting on the forum and no reply, no nothing, even when I said “Hello, this is (student’s name) at the beginning of the course, I didn’t even receive an “ok, welcome” so it was a bit strange like you don’t want me around, yes I got no feedback, I felt like I was in the dark…
\n
\n
\n
Vague & confusing
\n
E: for this course I am trying to finish today, it’s basically, try and answer this question give your own views and I am a bit confused …like, what do you mean, give my own views? A couple of other students did ask the lecturer but to be honest I was still not sure so I’m just kind of giving support by what I find in the readings and giving a little bit about my own views
\n
\n\n
Table 4.
Pedagogical dimensions of feedback: negative features.
\n
In the third subtheme, students identify and distinguish between the different functions of feedback in relation to their learning. These functions include affective, corrective, informative, and reflective feedback (Table 5).
\n
\n
\n
\n
\n\n
\n
Subthemes
\n
\n
Student comments: examples
\n
\n\n\n
\n
Functions
\n
Affective
\n
A: … and it was after midnight and I thought oh my god, she is not sleeping, honestly, so that was amazing really because when I asked for an extension I was really stressed, but it’s nice to have somebody saying ‘alright, calm down, I am there, let’s see what we can do’.
\n
\n
\n
Corrective
\n
T: and also as a student I need to know why you are giving me this specific feedback, to explain to me why that was wrong and where can I find the correct answer…
\n
\n
\n
Informative
\n
M: The lecturer also referred us to other links for extra information.
\n
\n
\n
Reflective
\n
T: I like the comments “well done” or “nice thoughts but I also like it when lecturers ask us questions like “how about this?”
\n
\n\n
Table 5.
Pedagogical dimensions of feedback: different functions.
\n
\n
\n
4.2.2. Contextual dimensions
\n
In relation to the contextual dimensions of learning, students identified how the cultural context, teaching context, and their work and home life contexts played a role in their perception of feedback given (Table 6).
\n
\n
\n
\n
\n\n
\n
Theme
\n
Sub-themes
\n
Student comments: examples
\n
\n\n\n
\n
Context
\n
Cultural
\n
A: maybe it’s a Greek thing but I wouldn’t expect a student of mine to say alright I have 99% what went wrong? I would say what the hell are you talking about, this is magnificent but yes, I wanted more explanation and not just a grade but obviously it’s not the lecturer’s problem, its mine…
\n
\n
\n
Teaching
\n
M: I don’t know if we can expect quick feedback if there are like 50 students and one professor… E: the response was kind of vague and I guess because I had been a teacher at a university before I was kind of timid about following up on my confusion and not wanting to be annoying so I kind of just plodded on because I know how it is to get bugged by students when I am expecting them to understand and they’re not understanding…
\n
\n
\n
Work/home life
\n
C: I sent a message, and I got no reply, saying that because of a family situation, I need a big extension because I am many hours in the clinic, I’m outside the home and I have everything going on in my head E: I mean people have got their jobs and family obligations, whatever, and so forth, and professors will say they’re giving them plenty of time but they don’t understand what’s going on.
\n
\n\n
Table 6.
Contextual dimensions of feedback.
\n
\n
\n
4.2.3. Relational dimensions
\n
The third theme identified in the data, focuses on the relational dimensions of feedback. Within this theme, two subthemes emerge, showing the intrapersonal dimensions of feedback based on how students relate it to their personal experiences (Table 7).
\n
\n
\n
\n\n
\n
Subthemes
\n
Student comments
\n
\n\n\n
\n
Intrapersonal
\n
A: anyway when I was 24 I needed a lot of guidance not that I don’t need it now but I feel more confident about stuff that I have worked on extensively. E: I guess because I had been a teacher at a university before I was kind of timid about following up on my confusion and not wanting to be annoying so I kind of just plodded on because I know how it is to get bugged by students when I am expecting them to understand and they’re not understanding and you know, you know what I mean…its challenging but it’s my fault in a way, maybe I should be interacting with the professor more and asking questions and even for extensions
\n
\n\n
Table 7.
Intrapersonal dimensions of feedback.
\n
The other subtheme highlights the interpersonal dimension of feedback which shows how students construct knowledge through their interactions with their peers and the researcher/instructor (Table 8).
\n
\n
\n
\n
\n\n
\n
Subthemes
\n
Student comments
\n
\n\n\n
\n
Interpersonal
\n
Instructor
\n
M: To be honest my expectations were kind of different. I thought that I would not feel the presence of the lecturer and that she wouldn’t comment on my work. However, I was wrong. … during the course I had the feeling that the lecturer was not distant but someone who was eager to support the students E: I can see how too much involvement can create less motivation in students and less self-responsibility. So yeah, it would be good to have umm bit of involvement but then students should know… should be understanding that this is a matter of self-responsibility E: but I think for the first two or three weeks things are not very clear about the course and about how we ought to study, about the assignments and all the online activities which are our main worry, I think, so yes, I think we should have more interaction I think in the first couple of weeks
\n
\n
\n
Peers
\n
A: I really adore reading to work out what others have written because it opens a new door to things I haven’t thought before or things I have thought differently which is the thing I like and then I can see the feedback and see if that was a good direction I should have taken, yes I do so, very often E: but I just sense that from the students they are just a bit uneasy about criticizing, there’s more of a sense on the forum of trying to support each other instead of critiquing what they are writing. T: I have been in contact with only two people but we have really, really interesting conversations about the lectures and about our experiences … other students do not answer the questions, even though the question is not addressed to the lecturer, it’s not to “dear lecturer” its addressed to “dear everyone”, well I think it was to do with showing respect to the lecturer and not commenting on what they are about to say
\n
\n\n
Table 8.
Interpersonal dimensions of feedback.
\n
\n
\n
\n
\n
5. Discussion
\n
This study sets out to explore two research questions on the inter-relational nature of online feedback by examining what feedback was given by a course leader during a DL course and how this feedback was perceived by students.
\n
\n
5.1. What feedback was given?
\n
The first part of this study offers a descriptive insight into the online feedback practices in this specific distance learning course in terms of ‘what happened’. The amount of feedback given was fairly extensive, with students receiving feedback to over 86% of all their online postings. Moreover, this feedback was mostly personalized (71%), and based on length of messages, it was substantive, with the average length of a message over 100 words. Findings also show that feedback was timely with most students receiving a response within 2 days. In relation to the types of feedback given, it was overwhelmingly informative, focused on providing information about course content and procedures (40%). Other types of feedback were also evident to a lesser extent including affective feedback that focused on motivating and supporting students (11%), corrective feedback (7%), and reflective feedback that asked questions (4%). Finally, it is also noteworthy that the flow of feedback was unidirectional originating from the researcher/instructor to the students. The data show little evidence of student participation in the feedback process other than superficial and infrequent instances of “I agree with …” or “[name] makes a good point.” Although students were encouraged to interact with each other and ask and answer questions addressed to each other, this did not occur in the public space of the course forum, although interview data suggest that students did message and support each other privately: “through the whole Master’s programme I have been in contact with two people but we have really, really interesting conversations about the lectures and about our experiences, I think it would be helpful if we had a way to connect to each other.” Many aspects of these findings are supported by the literature which advocates that effective online feedback is personalized [13], substantive [41], and timely [37, 40]. However, findings also show that the limited amount of reflective feedback—asking students questions about their learning—challenges the notion of best practice. Indeed, the importance of building reflection into a distance learning course is important in helping learners develop metacognitive skills that encourage self and peer evaluation [42]. Another finding that challenges best practice relates to the unidirectional flow of feedback from researcher/instructor to students and lack of mutually constructed feedback between students. This lack of participation may be due to numerous factors reported in the literature, such as course design not based on constructivist pedagogy [45] and lack of incentive and/or knowledge of feedback strategies [37]. In addition, DL students’ perceptions of who should give feedback, discussed below, may also offer insights into their lack of participation.
\n
\n
\n
5.2. How was feedback received?
\n
The second part of this study offers insights into students’ perceptions of online feedback practices and the extent to which it influenced their learning. Analysis of these findings shows how these students interpret feedback according to specific pedagogical, contextual, and relational dimensions of learning.
\n
In relation to the pedagogical dimensions of feedback, students’ positive perceptions of this feedback are very much in line with concepts of best practice described in the literature. In line with the literature [36], students perceived feedback as being accessible, in that they could read their own and other students’ feedback at any point throughout the course: “before the exams and during the study period I am trying to read all the messages because I want to see if I am on the right path or not.” Similarly, as reported in the literature, students comment that feedback was focused [11], motivating [23, 43, 47], personalized [9], timely [37, 40], and useful [44] (see Table 3). Students’ more negative perceptions (see Table 4) are also documented in the literature with students making particular references to some of the feedback they received as being critical in tone [23, 43, 47], inconsistent, inconvenient, lacking [37, 40], and vague and confusing [44]. In addition, these DL students were aware of the different pedagogical functions of feedback and identified the value of affective, corrective, informative, and reflective feedback on their learning (see Table 5). This is also supported by the literature of best practice in asynchronous learning environments [33].
\n
Next, in relation to the contextual dimensions of feedback (see Table 6), students perceived online feedback through the lens of their own cultural expectations with one student commenting that despite achieving a top grade “…maybe it’s a Greek thing but […] I wanted more explanation not just a grade.” The disappointment with the lack of feedback suggested by this comment is reinforced by findings in the literature which suggest that online learning can be a lonely place for learners whose cultural experiences and expectations may differ from the dominant educational culture [53]. Also, in relation to context, students perceived that the quality and quantity of feedback were dependent of class size and the perceived work load of their instructor with one student commenting “I don’t know if we can expect quick feedback if there are like 50 students and one professor.” While student numbers in this particular course may be low, one student perceived a demanding work load for instructors with another student commenting “because I had been a teacher at a university before […] I kind of just plodded on because I know how it is to get bugged by students.” The perception of overworked and unavailable faculty is echoed by research that reports that the massification of online education and the increasing workload means they are more likely to sacrifice formative feedback as part of their online teaching [48, 54]. Finally, some DL students perceive that the feedback they received lacked understanding of their particular work/home life situations with one student commenting “I sent a message, and I got no reply, saying that because of a family situation, I need a big extension because I am many hours in the clinic, I’m outside the home and I have everything going on in my head,” while another students said “I mean people have got their jobs and family obligations, whatever, and so forth, and professors will say they’re giving them plenty of time but they don’t understand what’s going on.” This lack of understanding for students’ lives outside their studies is cited in the literature as reasons for high student attrition and low online course completion [25, 55].
\n
Finally, the findings also highlight students’ perceptions of feedback as constructed through their relationships with self and others. In the first instance, students comment on their intrapersonal dimensions of knowledge construction and the awareness of autonomous learning (see Table 7). One student comments on her growing confidence and the need for less guidance, as she gets older: “anyway when I was 24 I needed a lot of guidance, not that I don’t need it now, but I feel more confident about stuff that I have worked on extensively,” while another student turns to self-reliance rather than inconvenience the course leader: “I kind of just plodded on because I know how it is to get bugged by students.” A greater understanding of how students perceive their own learning in relation to guidance and support is important, since such perceptions are strongly associated their learning outcomes [13].
\n
In relation to students’ perceptions of feedback as constructed interpersonally with their peers and course leader (see Table 8), findings suggest that feedback is an indication of teaching presence “To be honest my expectations were kind of different. I thought that I would not feel the presence of the lecturer and that she wouldn’t comment on my work. However, I was wrong, during the course I had the feeling that the lecturer was not distant but someone who was eager to support the students.” The implications of this are highlighted in the literature, which indicates that teaching presence is strongly associated with course satisfaction and learning outcomes [56, 57]. However within the research [58, 59], there is also a caveat that too much teaching presence may deter autonomous leaning and self-reliance, and this is echoed in this study: “I can see how too much involvement can create less motivation in students and less self-responsibility.”
\n
The interpersonal dimension of feedback also includes students’ engagement with their peers. Findings indicate that although students value reading work posted by their peers, “I really adore… reading to work out what others have written because it opens a new door to things I haven’t thought before or things I have thought differently which is the thing I like and then I can see the feedback and see if that was a good direction I should have taken,” they did not participate in the feedback process. Indeed, this also borne out in research which suggests that most students enrolled in online or distance learning courses are lurking; with the main reason given for logging on is to look at questions and tasks, as well as read other students’ posts and instructor feedback. The same research suggests that only one in four are willing to contribute to discussion [36]. These findings are contrary to other research which suggests that students identify effective feedback as a mutual process [10], and it indicates an inconsistency between students’ perceptions and their actual practices. Indeed, findings suggest that these students perceive feedback as a top-down process with distinct roles assigned to students and educators. One student comments “but I just sense that from the students they are just a bit uneasy about criticizing, there’s more of a sense on the forum of trying to support each other instead of critiquing what they are writing.” While another student adds “when we write a question to a lecturer, other students do not answer the question, they are waiting for the lecturer to answer that even though the question is not addressed to the lecturer … its not to “dear lecturer” its addressed to “dear everyone”, well I think it was to do with showing respect to the lecturer and not commenting on what they are about to say.” It might also be that lack of collaborative learning in the feedback process is part of students’ preferred learning style, and there is evidence to suggest that successful online learners often exhibit a preference for an independent over a collaborative learning style [38].
\n
\n
\n
\n
6. Conclusions
\n
This study set out to explore the practices and perceptions of feedback given during a DL course for postgraduate students with the aim of offering insights and making suggestions for improving teaching. There are several conclusions which can be drawn from this study.
\n
First, from an evaluative perspective, findings show that in terms of the frequency, length, and types of feedback given, it is largely in line with notions of best practice as outlined in the literature. However, findings also reveal some shortcomings in the type of feedback given, namely that there was limited reflective feedback that asked students direct and indirect questions so as to reflect on their understandings of the course. It should be stressed, however, that the main purpose of this study was not to evaluate the performance of the researcher/instructor but rather to offer a professional development perspective. By making explicit the nature of the feedback given, the course leader has the opportunity to reflect on her feedback practices, which are typically intuitive and spontaneous.
\n
Second, this study also shows the extent to which students’ perceptions of feedback align with notions of best practice. These perceptions offer a variety of experiences and understandings of feedback that express both positive and negative points. In themselves, these perceptions cannot offer a complete and unbiased picture of the quality of feedback; however, they offer valuable insights into how feedback is perceived by students and why it is perceived in such a way. This is especially important in DL Education courses, when some students may be practicing teachers with their own feedback practices and beliefs. In such cases, it is important for course leaders to be aware of how these practices and beliefs align or differ from their own.
\n
Third, the use of an analytic frame to consider the pedagogical, contextual, and relational dimensions of feedback broadens the scope by which feedback is typically discussed. Findings show how these three dimensions act as a lens through which students are able to mediate and understand their feedback experience. Such a frame can also be used as a developmental tool to help educators consider how their online feedback practices may support or inhibit student learning. Questions such as the following can guide educators to examine their own online feedback practices:
Pedagogically: Is online feedback accessible to all, focused, motivating, personalized, timely, and useful? What type of feedback is offered and how does it support student learning? E.g., To what extent does it provide information about course content and/or procedures? To what extent does it emotionally support and motivate students? To what extent does it positively and/or negatively evaluate student work? To what extent does it ask questions and encourage students to reflect on their learning?
Contextually: To what extent does the online feedback reflect the values of a specific educational culture? Does feedback take into account the experiences and expectations of learners from other educational cultures? To what extent is the quality and quantity of feedback shaped by the specific demands of teaching context? To what extent does feedback consider the external work/home life pressures face DL students?
Relationally: How do students feel about the online feedback they receive? To what extent is your teaching presence established through feedback? What kind of presence is it? What is the role of other students in the feedback process? Is feedback mutually constructed by instructor and students and between students? Why/why not?
\n
It is important to acknowledge some of the limitations of this study, so that they may encourage further research. First, the role of the instructor as researcher and instructor may raise concerns about objectivity of data. However, efforts made to minimize bias ensure a standard of rigor with the use of retrospective data collection and an independent rater. Interviews were also held with participants after course completion, when students would have no further contact with the particular instructor. Second, the focus on perceptions can only represent the views of a small sample of DL students. Similarly, findings can only represent the feedback practices of one particular instructor on one particular DL course. As such, the idiosyncratic nature of the data does not provide a basis for transferability and generalizability to other courses or other instructors teaching on the same or a similar program. In spite of these concerns, this choice of research methods offered the researcher/instructor the opportunity to analyze her own online teaching and gain insights into the practices and processes of giving feedback and how these might be perceived by DL students to support or inhibit their learning.
\n
In educational institutions around the world, the challenges of online teaching have become a reality for many instructors, and as such, it is hoped that by sharing these results, other instructors will take the opportunity to inquire into their online feedback practices. It is also hoped that the issues raised in this study will also pose questions for further research in this area to enhance online teaching.
\n
\n\n',keywords:"feedback, online education, distance learning, pedagogy, perceptions",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/59369.pdf",chapterXML:"https://mts.intechopen.com/source/xml/59369.xml",downloadPdfUrl:"/chapter/pdf-download/59369",previewPdfUrl:"/chapter/pdf-preview/59369",totalDownloads:256,totalViews:216,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 22nd 2016",dateReviewed:"January 15th 2018",datePrePublished:null,datePublished:"July 18th 2018",readingETA:"0",abstract:"While feedback is recognized as an important part of the pedagogical process in supporting student learning, it is a relatively a new area of online education research. The purpose of this mixed-methods study was to examine online feedback processes from the vantage points of the course instructor and a cohort of students. Based on data collected from the online forums and student interviews, the researcher/instructor sets out to determine what feedback was given and how it was perceived by students. Evidence suggests that the feedback given was largely aligned with research definitions of “best practice” in terms of being timely, accessible and substantial. In terms feedback type, it was informative, supportive, corrective, and, to a lesser extent, reflective. However, evidence suggests that the scope of students’ perceptions of effective feedback was broader than suggested in the research literature with feedback viewed in relation to specific pedagogical, contextual, and relational dimensions. It is suggested that analysis of feedback from these two vantage points is important for instructors wishing to enhance their online teaching. It is also suggested that a frame for understanding the effect of online feedback on student learning should be broadened to consider its wider pedagogical, contextual, and relational dimensions.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/59369",risUrl:"/chapter/ris/59369",book:{slug:"advanced-learning-and-teaching-environments-innovation-contents-and-methods"},signatures:"Christine Savvidou",authors:[{id:"1264",title:"Dr.",name:"Christine",middleName:null,surname:"Savvidou",fullName:"Christine Savvidou",slug:"christine-savvidou",email:"savvidou.c@unic.ac.cy",position:null,institution:{name:"University of Nicosia",institutionURL:null,country:{name:"Cyprus"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Background",level:"1"},{id:"sec_2_2",title:"2.1. Terminology",level:"2"},{id:"sec_3_2",title:"2.2. Feedback types",level:"2"},{id:"sec_4_2",title:"2.3. Delivering online feedback",level:"2"},{id:"sec_5_2",title:"2.4. Characteristics of effective online feedback",level:"2"},{id:"sec_6_2",title:"2.5. Challenges and concerns",level:"2"},{id:"sec_8",title:"3. Methodology",level:"1"},{id:"sec_8_2",title:"3.1. Purposes of study and research questions",level:"2"},{id:"sec_9_2",title:"3.2. Context",level:"2"},{id:"sec_10_2",title:"3.3. Participants",level:"2"},{id:"sec_11_2",title:"3.4. Procedures",level:"2"},{id:"sec_11_3",title:"Table 1.",level:"3"},{id:"sec_12_3",title:"3.4.2. Phase 2",level:"3"},{id:"sec_15",title:"4. Findings",level:"1"},{id:"sec_15_2",title:"4.1. Feedback given",level:"2"},{id:"sec_16_2",title:"4.2. Feedback received",level:"2"},{id:"sec_16_3",title:"Table 3.",level:"3"},{id:"sec_17_3",title:"Table 6.",level:"3"},{id:"sec_18_3",title:"Table 7.",level:"3"},{id:"sec_21",title:"5. Discussion",level:"1"},{id:"sec_21_2",title:"5.1. What feedback was given?",level:"2"},{id:"sec_22_2",title:"5.2. How was feedback received?",level:"2"},{id:"sec_24",title:"6. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Askew S, editor. Feedback for Learning. London: Psychology Press; 2000\n'},{id:"B2",body:'Askew S, Lodge C. Gifts, ping-pong and loops – linking feedback and learning. In: Askew S, editor. Feedback for Learning. London: Routledge; 2000:1-17\n'},{id:"B3",body:'Gibbs G, Simpson C. Conditions under which assessment supports students’ learning. Learning and Teaching in Higher Education. 2005;1:3-1\n'},{id:"B4",body:'Juwah C, Macfarlane-Dick D, Matthew B, Nicol D, Ross D, Smith B. Enhancing student learning through effective formative feedback\n'},{id:"B5",body:'Scott SV. Practising what we preach: Towards a student-centred definition of feedback. Teaching in Higher Education. 2014 Jan;19(1):49-57\n'},{id:"B6",body:'Weibell CJ. Principles of learning: 7 principles to guide personalized, student-centered learning in the technology-enhanced, blended learning environment. 2011 [cited 2018 Feb 12]. 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Enhancing the impact of formative feedback on student learning through an online feedback system. Electronic Journal of e-Learning. 2010;8(2):111-122\n'},{id:"B12",body:'Hixon E, Ralston-Berg P, Buckenmeyer J, Barczyk C. The impact of previous online course experience on students’ perceptions of quality. Online Learning. 2016;20(1):25-40\n'},{id:"B13",body:'Rowe AD, Wood LN. Student perceptions and preferences for feedback. Asian Social Science. 2009 Feb 10;4(3):78\n'},{id:"B14",body:'Allen IE, Seaman J. Changing Course: Ten Years of Tracking Online Education in the United States. Sloan Consortium; Newburyport, MA. 2013 Jan\n'},{id:"B15",body:'Sellani RJ, Harrington W. Addressing administrator/faculty conflict in an academic online environment. The Internet and Higher Education. 2002 Aug;5(2):131-145\n'},{id:"B16",body:'Lao T, Gonzales C. Understanding online learning through a qualitative description of professors and students’ experiences. Journal of Technology and Teacher Education. 2005 Jul;13(3):459\n'},{id:"B17",body:'Wegmann S, McCauley J. Shouting through the fingertips: Computer-mediated discourse in an asynchronous environment. In: Society for Information Technology & Teacher Education International Conference. Association for the Advancement of Computing in Education (AACE); 2008 March. pp. 805-808\n'},{id:"B18",body:'Keengwe J, Kidd TT. Towards best practices in online learning and teaching in higher education. Journal of Online Learning and Teaching. 2010;6(2):533\n'},{id:"B19",body:'Levy S. Six factors to consider when planning online distance learning programs in higher education. Online Journal of Distance Learning Administration [Internet]. Spring 2003;6(1):1-19. [cited 2018 Feb 12]. Available from: http://aris.teluq.uquebec.ca/Portals/598/t3_levy2003.pdf\n\n'},{id:"B20",body:'Anderson T, editor. The Theory and Practice of Online Learning. Athabasca: University Press; 2008\n'},{id:"B21",body:'Anagnostopoulou E, Mavroidis I, Giossos Y, Koutsouba M. Student satisfaction in the context of a postgraduate programme of the Hellenic Open University. Turkish Online Journal of Distance Education. 2015;16(2):40-55\n'},{id:"B22",body:'Dziuban C, Moskal P, Thompson J, Kramer L, DeCantis G, Hermsdorfer A. Student satisfaction with online learning: Is it a psychological contract? Online Learning. 2015 Mar;19(2):n2\n'},{id:"B23",body:'Mahmood A, Mahmood ST, Malik ABA. Comparative study of student satisfaction level in distance learning and live classroom at higher education level. Turkish Online Journal of Distance Education. 2012;13(1):128-136\n'},{id:"B24",body:'Mentkowski M, Rogers G, Doherty A, Loacker G, Hart JR, Rickards W, O’Brien K, Riordan T, Sharkey S, Cromwell L, Diez M. Learning that Lasts: Integrating Learning, Development, and Performance in College and Beyond. San Francisco, CA: Jossey-Bass; 2000\n'},{id:"B25",body:'Fetzner M. What do unsuccessful online students want us to know? Journal of Asynchro-nous Learning Networks. 2013 Jan;17(1):13-27\n'},{id:"B26",body:'Skinner BF. Why we need teaching machines. Harvard Educational Review. 1961;31:377-398\n'},{id:"B27",body:'Ausubel DP. A cognitive view. Educational Psychology. New York: Holt, Rinehart and Winston; 1968\n'},{id:"B28",body:'Bruner JS. The Process of Education. Cambridge, MA: Harvard University Press; 2009 Jun 30\n'},{id:"B29",body:'Mason BJ, Bruning R. Providing feedback in computer-based instruction: What the research tells us. Center for Instructional Innovation, University of Nebraska–Lincoln; 2001:14. [cited 2018 Feb 12]. Available from: http://dwb.unl.edu/Edit/MB/MasonBruning.html\n\n'},{id:"B30",body:'Wiggins G. Seven keys to effective feedback. Feedback. 2012 Sep;70(1):10-16\n'},{id:"B31",body:'Kulhavy RW, Stock WA. Feedback in written instruction: The place of response certitude. Educational Psychology Review. 1989 Dec;1(4):279-308\n'},{id:"B32",body:'Biggs JB. Teaching for Quality Learning at University: What the Student Does. UK: McGraw-Hill Education; 2011 Sep 1\n'},{id:"B33",body:'Blignaut S, Trollip SR. Developing a taxonomy of faculty participation in asynchronous learning environments—An exploratory investigation. Computers & Education. 2003 Sep;41(2):149-172\n'},{id:"B34",body:'Shute VJ. Focus on formative feedback. Review of Educational Research. 2008 Mar;78(1):153-189\n'},{id:"B35",body:'Brookes M. An evaluation of the impact of formative feedback podcasts on the student learning experience. Journal of Hospitality, Leisure, Sports and Tourism Education (Pre-2012). 2010 Apr;9(1):53\n'},{id:"B36",body:'Owens JD, Price L. Is e-learning replacing the traditional lecture? Education+ Training. 2010 Mar 16;52(2):128-139\n'},{id:"B37",body:'Coll C, Rochera MJ, de Gispert I. Supporting online collaborative learning in small groups: Teacher feedback on learning content, academic task and social participation. Computers & Education. 2014 Jun;75:53-64\n'},{id:"B38",body:'Beyth-Marom R, Saporta K, Caspi A. Synchronous vs. asynchronous tutorials: Factors affecting students’ preferences and choices. Journal of Research on Technology in Education. 2005 Mar;37(3):245-262\n'},{id:"B39",body:'Sorensen EK, Takle ES. Investigating knowledge building dialogues in networked communities of practice. A collaborative learning endeavor across cultures. Interactive Educational Multimedia: IEM. 2005;10:50-60\n'},{id:"B40",body:'De Gagne JC, Walters KJ. The lived experience of online educators: Hermeneutic phenomenology. Journal of Online Learning and Teaching. 2010 Jun;6(2):357\n'},{id:"B41",body:'Mokoena S. Engagement with and participation in online discussion forums. TOJET: The Turkish Online Journal of Educational Technology. 2013 Apr 1;12(2):97-105\n'},{id:"B42",body:'Akin L, Neal D. CREST+ model: Writing effective online discussion questions. Journal of Online Learning and Teaching. 2007 Jun;3(2):191-202\n'},{id:"B43",body:'Cavanaugh C, Blomeyer RL, editors. What Works in K-12 Online Learning. Eugene, OR: International Society for Technology in Education; 2007\n'},{id:"B44",body:'Carless D. Differing perceptions in the feedback process. Studies in Higher Education. 2006 Apr;31(2):219-233\n'},{id:"B45",body:'Garrison R. Implications of online and blended learning for the conceptual development and practice of distance education. International Journal of E-Learning & Distance Education. 2009 Jul;23(2):93-104\n'},{id:"B46",body:'Hara N. Student distress in a web-based distance education course. Information, Communication & Society. 2000 Jan;3(4):557-579\n'},{id:"B47",body:'Roper AR. How students develop online learning skills. Educause Quarterly. 2007 Nov;30(1):62\n'},{id:"B48",body:'Betts K. Why do faculty participate in distance education. The Technology Source. Oct 1998. [cited 2018 Feb 13]. Available from: http://technologysource.org/article/429/\n\n'},{id:"B49",body:'Knupfer NN, McLellan H. Descriptive research methodologies. Handbook of research for educational communications and technology. 1996:1196-1212\n'},{id:"B50",body:'Lincoln YS. Guba EG. Naturalistic inquiry. Beverly Hills, CA: Sage; 1985 Apr\n'},{id:"B51",body:'Johnson RB, Onwuegbuzie AJ, Turner LA. Toward a definition of mixed methods research. Journal of Mixed Methods Research. 2007 Apr;1(2):112-133\n'},{id:"B52",body:'Cohen D, Crabtree B. Qualitative Research Guidelines Project. 2006 July [cited 2018 Feb 13]. Available from: http://www.qualres.org/HomeEval-3664.html\n\n'},{id:"B53",body:'Shattuck K. Glimpses of the global coral gardens: Insights of international adult learners on the interactions of cultures in online distance education. PhD [dissertation]. University Park, PA: The Pennsylvania State University; 2005\n'},{id:"B54",body:'Nagel L, Kotzé TG. Supersizing e-learning: What a CoI survey reveals about teaching presence in a large online class. The Internet and Higher Education. 2010 Jan;13(1):45-51\n'},{id:"B55",body:'Kahu ER, Stephens C, Zepke N, Leach L. Space and time to engage: Mature-aged distance students learn to fit study into their lives. International Journal of Lifelong Education. 2014 Jul;33(4):523-540\n'},{id:"B56",body:'Shea P, Li CS, Swan K, Pickett A. Developing learning community in online asynchronous college courses: The role of teaching presence. Journal of Asynchronous Learning Networks. 2005 Dec;9(4):59-82\n'},{id:"B57",body:'Baker C. The impact of instructor immediacy and presence for online student affective learning, cognition, and motivation. Journal of Educators Online. 2010 Jan;7(1):n1\n'},{id:"B58",body:'Mazzolini M, Maddison S. Sage, guide or ghost? The effect of instructor intervention on student participation in online discussion forums. Computers & Education. 2003 Apr 30;40(3):237-253\n'},{id:"B59",body:'Sahu C. An evaluation of selected pedagogical attributes of online discussion boards. Proceedings ascilite Melbourne. 2008:861-865\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Christine Savvidou",address:"savvidou.c@unic.ac.cy",affiliation:'
University of Nicosia, Nicosia, Cyprus
'}],corrections:null},book:{id:"6161",title:"Advanced Learning and Teaching Environments",subtitle:"Innovation, Contents and Methods",fullTitle:"Advanced Learning and Teaching Environments - Innovation, Contents and Methods",slug:"advanced-learning-and-teaching-environments-innovation-contents-and-methods",publishedDate:"July 18th 2018",bookSignature:"Núria Llevot-Calvet and Olga Bernad Cavero",coverURL:"https://cdn.intechopen.com/books/images_new/6161.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"193390",title:"Ph.D.",name:"Núria",middleName:null,surname:"Llevot-Calvet",slug:"nuria-llevot-calvet",fullName:"Núria Llevot-Calvet"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"58311",title:"Pedagogical and E-Learning Techniques for Quality Improvement of ICT 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\n
1. Introduction
\n
Nowadays most of generated electricity comes from nonrenewable sources of fuel. These products transfer to the atmosphere important quantities of CO2, and inescapably leading to the warming up of the atmosphere [1]. The production of the wind energy spreads through the world, and significantly, it imposed itself during the past decade [2]. Doubly-fed induction generators (DFIGs) are actually the most used wind power generators in many countries [3].
\n
Therefore, many contributions have been made to the inverters and converters usually in DFIG used in the power electronics domain [4]. A doubly fed induction generator model for transient stability analysis has been proposed in [5], in which authors focused their study on the control loops of instantaneous response. In [6], authors have been proposed some robust observers to estimate states and actuator faults for different class of linear and nonlinear systems at the same instant. Though systems are becoming more and more complex, DFIG can be subject by many types of faults [7], diagnosis and faults estimation issues have become primordial to ensure a good supervision of systems and guarantee the safety of materials and operators (humans) [8].
\n
A survey based on current sensor fault detection and isolation and control reconfiguration current for doubly fed induction generator has been proposed by [9]. Studies led by [10], have contributed to an adaptive parameter estimation algorithm used for estimating the rotor resistance of the DFIG, however, the others parameters were assumed to be constant. To improve the extended Kalman filter (EKF), a new nonlinear filtering algorithm named the unscented Kalman filter (UKF) has been developed in [11]. Widely used in some fields, UKF has been found in several studies such as training of neural networks [12], multi-sensor fusion for instance.
\n
This chapter investigates the usage of the unscented Kalman filter UKF, high gain observer (HGO) and the moving horizon estimator (MHE) to estimate the dynamic states and electrical parameters of the wind turbine system. These estimates can be used to enhance the performance of doubly fed induction generator in power systems, for rotor and stator resistances faults in the circumstances where internal states will be involved in a control design [3] and the acquisition of internal states, which are relatively difficult to get can realized from the dynamic state estimation and for monitoring purposes. The chapter is organized as follows: in Section 2, the mathematical model for DFIG is presented, followed by the description of estimation algorithms in Section 3. The results of the parameter estimation tests are presented in Section 4. Finally Section 5 gives the conclusions.
\n
\n
\n
2. Mathematical model for DFIG
\n
In this section, we deal with the mathematical modeling of the DFIG-based wind energy system, we will only describe the wind turbine (also called drive train), and the asynchronous generator (also called induction generator) because this chapter focuses on estimating of the parameters and dynamic states of the DFIG Figure 1. Two frames of reference are used in this model: stator voltage (d-q) reference frame and mutual flux (d-q) reference frame. In Tables 1 and 2, all parameters and constants are given.
\n
Figure 1.
Configuration of DFIG-based wind turbine system [13].
\n
\n
\n
\n\n
\n
Parameters
\n
Values
\n
\n\n\n
\n
Rated active power (Ps)/(MW)
\n
1.5
\n
\n
\n
Rated voltage (line to line) (Vs)/(V)
\n
575
\n
\n
\n
Rated DC-link voltage (Vdc)/(V)
\n
1200
\n
\n
\n
Number of poles
\n
4
\n
\n
\n
Frequency (f)/(Hz)
\n
60
\n
\n
\n
Stator resistance (Rs)/(pu)
\n
0.00707
\n
\n
\n
Rotor resistance (Rr)/(pu)
\n
0.005
\n
\n
\n
Stator leakage inductance (Ls)/(pu)
\n
0.171
\n
\n
\n
Rotor leakage inductance (Lr)/(pu)
\n
0.156
\n
\n
\n
Magnetizing inductance (Lm)/(pu)
\n
2.9
\n
\n
\n
DC-link capacitance (C)/(F)
\n
0.04
\n
\n\n
Table 1.
Parameters of the DFIG.
\n
\n
\n
\n\n
\n
Parameters
\n
Values
\n
\n\n\n
\n
Rated wind speed (vw)/(m s−1)
\n
12
\n
\n
\n
Number of blade
\n
3
\n
\n
\n
Radius of blade (R)/m
\n
35.25
\n
\n
\n
Gearbox gain (G)
\n
91
\n
\n
\n
Moment of inertia (Jeq)/(kg m2)
\n
1000
\n
\n
\n
Viscosity factor (feq)/(N m s rad−1)
\n
0.0024
\n
\n\n
Table 2.
Parameters of the wind turbine.
\n
\n
2.1. Modeling of the wind turbine
\n
From the wind, the power extracted can give the mechanical torque. The energy from the wind is extracted from the wind turbine and converted into mechanical power [14]. The wind turbine model is based on the output power characteristics, as Eqs. (1) and (2), [15].
where the aerodynamic extracted power is Pm, which depends on CP, the efficiency coefficient,the air density ρ, the turbine swept area A, and the wind speed νw. The kinetic energy contained in the wind at a particular wind speed is given by Ew. The blade radius and angular frequency of rotational turbine are R and wt, respectively. CP(λ; β) the efficiency coefficient depends on tip speed ratio λTS and blade pitch angle β, determines the amount of wind kinetic energy that can be captured by the wind turbine system [13]. CP(λ; β) can be described as:
For the induction generator, the Park model is the model that is commonly used [16]. After applying the synchronously rotating reference frame transformation to the stator and rotor fluxes equations of the generator, the following differential equations describe the dynamics of the rotor and stator fluxes [17]:
where ws = 1 is the synchronous angular speed in the synchronous frame and wb = 2πf rad/s is the base angular speed, with f = 60 Hz. With additional variables stator-rotor mutual flux Φdm and Φqm, rotor current idr and iqr and stator current ids and iqs can be expressed as:
These equations are derived in [4] and all parameters are defined in per unit based on the generator ratings and synchronous speed.
\n
\n
\n
\n
3. Estimation algorithms
\n
\n
3.1. High gain observer
\n
This observer class is applied for nonlinear system classes of the form Eq. (12). Its applications are so large [18, 19]. We briefly present the developed survey in [20] that points up the synthesis of observers adapted to the observable nonlinear systems. Consider the following nonlinear system:
where \n\nx\n∈\n\nR\nn\n\n,\nu\n∈\n\nR\nm\n\n,\ny\n∈\n\nR\ns\n\n\n.
\n
First, the system Eq. (12) must be uniformly locally observable, and then it will be possible to make the variable change z = Γ(x) that will transform the system Eq. (12) in the following form:
The observer must satisfy the following theorem [20]:
The function \n\nφ\n\n is globally Lipschitz uniformly to u.
\n
Let \n\nK\n=\n\n\n\n\n\nK\n1\n\n\n\n\n\n\n\n\n⋱\n\n\n\n\n\n\n\n\nK\np\n\n\n\n\n\n\n an adequate size matrix such as, for every Ki block, the matrix.
\n
Ak − KkCk should give all its eigenvalues with negative real part:
\n
Let’s suppose that there exists two integer sets \n\n\n\n\nσ\n\n1\n,\n⋯\n,\n\n\n\nσ\nn\n\n∈\nZ\n\n\n\n and \n\n\n\n\nδ\n1\n\n>\n0\n\n⋯\n\n\nδ\np\n\n>\n0\n∈\n\nN\n∗\n\n\n\n\n such as:
\n\n\nΓ\n=\n\n\n\nh\n1\n\n\n\nL\nf\n\n\nh\n1\n\n\n\n\nL\nf\n2\n\n\nh\n1\n\n\n…\n\n\nL\nf\n\nδ\n1\n\n\n\nh\n1\n\n\n\nh\n2\n\n\n\nL\nf\n\n\nh\n2\n\n\n\n\nL\nf\n2\n\n\nh\n2\n\n\n…\n\n\nL\nf\n\nδ\n2\n\n\n\nh\n2\n\n\n…\n\nh\np\n\n\n\nL\nf\n\n\nh\np\n\n\n\n\nL\nf\n2\n\n\nh\np\n\n\n…\n\n\nL\nf\n\nδ\np\n\n\n\nh\np\n\n\n\nT\n\n\n And \n\n\nL\nf\n\nδ\nk\n\n\n\n is the Lie \n\n\nδ\nk\ni\n\n\n derivative.
The unscented Kalman filter (UKF) has been essentially designed for the state estimation problems, and applied in some nonlinear control applications [11]. The unscented Kalman filter (UKF) compensates for approximation issues of the extended Kalman filter (EKF). A Gaussian random variable represents the state distribution, which is specified using a set of sample points chosen very carefully [12]. The unscented transformation (UT) is a method to estimate or calculate statistics of a random variable which is subjected to a nonlinear transformation [11]. In stochastic estimation problems, a common assumption usually is used which underline the fact that the process and measurement noise terms are additive, as in:
The dimension of the sigma-points is the same as the state vector, that is to say L = nx. The UKF is recursively executed, starting with the assumed initial conditions \n\n\n\nx\n̂\n\n0\n\n\n and P0. First a set of sigma-points are generated from the prior state estimate \n\n\n\nx\n̂\n\n\nk\n−\n1\n\n\n\n and covariance Pk − 1 at each discrete-time step, as in:
\n\n\n\nχ\n\nk\n,\nk\n−\n1\n\n\n\n means that this is the predicted value of the sigma-point based on the information from the prior time step. Sigma-points transformed, the post transformation mean and covariance are computed using weighted averages of the transformed sigma-points [21],
where \n\n\nη\n0\nm\n\n=\nλ\n/\n\n\nL\n+\nλ\n\n\n\n and \n\n\nη\n0\nc\n\n=\nλ\n/\n\n\nL\n+\nλ\n\n\n+\n1\n−\n\nα\n2\n\n+\nβ\n\n. The measurement noise is also omitted from the observation function, as for the prediction as in:
where is a matrix of output sigma-points. Output sigma-points are used to calculate output covariance matrix, the predicted output and cross-covariance by using:
Due to the additive noise assumption, R is added to the output covariance matrix. For calculating the Kalman gain matrix K, covariance matrices are used, using:
With yk, the measurement vector, \n\n\n\nx\n̂\n\nk\n\n\n is the a posteriori state and Pk is the covariance estimates.
\n
\n
\n
3.3. The moving horizon estimation
\n
The moving horizon estimation is a powerful means of estimating the states, and having in particular the possibility to constrain the outputs, states and noises. We can be described it as a least-squares optimization that leads to a states’ estimation and working with a limited amount of information. Its particularity is to avoid the recursive manner characteristic of the extended Kalman filter. Under different approaches, several researchers [22, 23, 24, 25, 26, 27, 28] studied it, however presenting many similarities. The moving and full state estimations almost follow the same steps. In the moving state estimation, variables can be handled contrary to the full state estimation. In the full state estimation, at current time k, all variables from initial time n = 0 to n = k are used in the calculation. With a horizon H, the moving state estimation uses in the calculation only the concerned variables (measured outputs, manipulated inputs and estimated states) from n = k + 1 − H to n = k, a moving vectors collect them. First of all, consider the full state estimation problem. Let assume that the process can be represented by the following continuous-time model [29, 30, 31]:
where the matrix C is the Jacobian matrix of h with respect to xk. In the full state estimation problem, we have to minimize the following criterion with respect to the sequence of noises \n\n\n\nw\n0\n\n…\n\nw\n\nk\n−\n1\n\n\n\n\n and to the initial state x0, and then the states \n\n\n\nx\n̂\n\ni\n\n\n are obtained by using Eq. (28).
The weighting matrices \n\n\nΠ\n0\n\n−\n1\n\n\n\n, \n\n\nQ\n\n−\n1\n\n\n\n and \n\n\nR\n\n−\n1\n\n\n\n, respectively, symbolize the initial estimation, the confidence in the dynamic model and the measurements. The main disadvantage of full state estimation is that during the computation we notice the size of the optimization problem grows as time increases, and would likely cause a failure in the optimization. The favorable solution to this increasing size is to set the problem according to a moving-horizon approach.
\n
Let us consider the problem of moving state estimation. The criterion Eq. (30) is split into two parts [24, 25]:
The second term Jmhe of the criterion Eq. (31) depends on the sequence of noises \n\n\n\nw\n\nk\n−\nH\n\n\n…\n\nw\n\nk\n−\n1\n\n\n\n\nand on the state xk−H. Assume that k > H and set the optimized criterion:
where z is the arrival state xk−H based on the optimized variables \n\n\n\nw\n\nk\n−\nH\n\n∗\n\n…\n\nw\n\nk\n−\nH\n−\n1\n\n∗\n\n\n\n and x0.
\n
In practice, it is very complicated and almost impossible to really minimize \n\n\nJ\n\nk\n−\nH\n\n\n\nz\n\n\n when k becomes large enough as this would be a full estimation problem again. The recommend solution is to retain the previous values of the optimized criterion \n\n\nJ\nk\n∗\n\n\n obtained by moving horizon estimation denoted by \n\n\nJ\nk\nmhe\n\n\nz\n\n\n along time k and to approximate \n\n\nJ\n\nk\n−\nH\n\n\n\nz\n\n\n as:
where \n\n\n\nx\n̂\n\n\nk\n−\nH\n\nmhe\n\n\n is the state estimated by moving horizon observer at time (k−H). Under these assumptions, the criterion Eq. (31) becomes:
With Π0 given. The Moving horizon estimation algorithm is described by the diagram in Figure 2.
\n
Figure 2.
Moving horizon estimation algorithm.
\n
\n
\n
\n
4. Numerical results
\n
In this section, the performances of the proposed observers are illustrated in simulation. Observers’ algorithms have been implemented in MATLAB/SIMULINK software. The doubly-fed induction generator system states which have been used for estimation are expressed into a vector x, this vector includes as parameters to estimate the stator and rotor resistances, as follows:
Table 3 shows a comparison of the running time of high gain observer (HGO), the unscented Kalman filter (UKF), and the moving horizon estimation (MHE) for the DFIG system. The high gain observer being the fastest among the three methods under various modes especially the healthy mode which represents a healthy DFIG and the faulty mode where stator and rotor resistance would have changed value during the operation of the DFIG. Tables 4 and 5 give the parameters of UKF and MHE only. For the UKF, the primary, secondary, and tertiary scaling parameters α, β and κ are chosen as 1, 2, and 0, respectively.
\n
\n
\n
\n
\n
\n\n
\n
\n
HGO
\n
UKF
\n
MHE
\n
\n\n\n
\n
Healthy mode
\n
1.200
\n
1.190
\n
152.978
\n
\n
\n
Faulty mode
\n
1.901
\n
1.666
\n
154.234
\n
\n\n
Table 3.
Running time of the three observers for the DFIG (in seconds).
\n
\n
\n
\n\n
\n
Parameters
\n
Values
\n
\n\n\n
\n
Weight matrix G
\n
eye(6)
\n
\n
\n
Covariance matrix P0
\n
3eye(6)
\n
\n
\n
Covariance matrix Q
\n
0.5eye(6)
\n
\n
\n
Covariance matrix R
\n
eye(5)
\n
\n
\n
Length horizon H
\n
10
\n
\n
\n
Initial guess
\n
[0; 0:5; 0:5; 1; 0:02; 0:02]
\n
\n\n
Table 4.
MHE parameters.
\n
\n
\n
\n\n
\n
Parameters
\n
Values
\n
\n\n\n
\n
Covariance matrix P0
\n
eye(6)
\n
\n
\n
Covariance matrix Q
\n
10−2diag([111110–410−4])
\n
\n
\n
Covariance matrix R
\n
10−2diag([11111])
\n
\n
\n
Initial guess
\n
[0; 0:5; 0:5; 1; 0:02; 0:02]
\n
\n\n
Table 5.
UKF parameters.
\n
Figures 3 and 4 show the generated estimates of the rotor and stator resistances by the HGO, UKF and the MHE in the healthy mode of working of the DFIG. Nevertheless, Figures 5 and 6 show the generated estimates of the rotor and stator resistances by the HGO, UKF and the MHE in the faulty mode of working, let us mention that faulty mode is simply a mode where the DFIG undergoes a fault on its stator and/or rotor resistances during the operation. We just simulated those scenarios to appreciate the estimation performance of different observers in particular the HGO, UKF and MHE for process monitoring or diagnostics purposes. We can observe that the estimates by the MHE converges to the actual parameters in fewer time compared to the HGO and UKF. In Table 3, we notice the total computation time to obtain an estimate for the HGO algorithm is about 1.200 seconds, for the UKF algorithm is also about 1.190 seconds while the MHE algorithm took 152.978 seconds to estimate the parameters in the normal mode of working, and in the faulty mode, we have about 1.901, 1.666 and 154.234 seconds for those observers, respectively. We can conclude that when the asynchronous machine has a stator or rotor resistance fault, the estimation time increases. The reason the MHE algorithm takes longer to make an estimate is that in simulation, the optimization of the objective function, through a nonlinear programming algorithm has been performed at each time step, in this case study the nonlinear programming algorithm used is the sequential quadratic programming in the MATLAB in-built function fmincon. For the HGO we can underline this, a big value of θ leads to consolidate the linear part and to guarantee the stability of the nonlinear part through the fact that \n\nφ\n\n is imposed globally Lipschitz in relation to x [27]. If θ are big enough, the time of convergence decreases, but the observation becomes extremely sensitive to the measurement noises. A small value of θ leads to the reverse effect obviously. In comparison with the extended Kalman filter, this observer contains a lot less of setting variables that facilitates its optimization. Besides the number of equations to solve are a lot weaker and it decreases the time of calculation considerably. To know that the number of differential equations to solve for the Kalman filter is of \n\nn\n+\n\n\nn\n\n\nn\n+\n1\n\n\n\n2\n\n\n such as, n is the size of observation vector, when that number is n for the high gain observer [18], for our experiment the value of the gain is θ = 27, on the other hand, the UKF algorithm has to handle.
\n
Figure 3.
Rotor resistance estimation in a healthy mode with HGO, UKF and MHE. (a) Rotor resistance estimation (HGO), (b) Rotor resistance estimation (UKF), (c) Rotor resistance estimation (MHE).
\n
Figure 4.
Stator resistance estimation in a healthy mode with HGO, UKF and MHE. (a) Stator resistance estimation (HGO), (b) Stator resistance estimation (UFK), (c) Stator resistance estimation (MHE).
\n
Figure 5.
Rotor resistance estimation in a faulty mode with HGO, UKF and MHE. (a) Rotor resistance estimation (HGO), (b) Rotor resistance estimation (UKF), (c) Rotor resistance estimation (MHE).
\n
Figure 6.
Stator resistance estimation in a faulty mode with HGO, UKF and MHE. (a) Stator resistance estimation (HGO), (b) Rotor resistance estimation (UKF), (c) Rotor resistance estimation (MHE).
\n
2 L + 1 sigma points and associated weights to represent state of the system. Tables 6 and 7 show the standard deviation and the variance of the estimation error. The comparison of these observers can be made by finding the mean squared error (MSE) value. The MSE can be evaluated as:
\n
\n
\n
\n
\n
\n\n
\n
\n
Std (HGO) × 10−5
\n
Std (UKF) × 10−5
\n
Std (MHE) × 10−5
\n
\n\n\n
\n
Rs
\n
350
\n
7450
\n
57.75
\n
\n
\n
Rr
\n
7.29
\n
3940
\n
41.38
\n
\n
\n
\n
Variance (HGO) × 10−5
\n
Variance (UKF) × 10−5
\n
Variance (MHE) × 10−5
\n
\n
\n
Rs
\n
1.26
\n
550
\n
0.033
\n
\n
\n
Rr
\n
0.0053
\n
160
\n
0.017
\n
\n\n
Table 6.
General statistics of the three observers (healthy mode).
\n
\n
\n
\n
\n
\n\n
\n
\n
Std (HGO) × 10−4
\n
Std (UKF) × 10−4
\n
Std (MHE) × 10−4
\n
\n\n\n
\n
Rs
\n
25
\n
8208
\n
270
\n
\n
\n
Rr
\n
4.86
\n
278
\n
26
\n
\n
\n
\n
Variance (HGO) × 10−5
\n
Variance (UKF) × 10−5
\n
Variance (MHE) × 10−5
\n
\n
\n
Rs
\n
0.62
\n
6737
\n
0.74
\n
\n
\n
Rr
\n
0.024
\n
77.47
\n
0.702
\n
\n\n
Table 7.
General statistics of the three observers (faulty mode).
where N is the number of time steps, n is the dimension of state vector, θi is the simulated value and \n\n\n\nθ\n̂\n\ni\n\n\n is the estimated value from the filters. Table 8 shows a comparison of the three observers by finding the mean squared error in the healthy and the faulty mode of operation of the DFIG and we can notice that generally, the mean squared error of states and parameters in faulty mode is relatively greater than those in the healthy mode because of the fault occurring suddenly during the operation, but we can always see the high performance of the moving horizon estimation on the others observers.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
HGO
\n
UKF
\n
MHE
\n
\n
\n
\n
Healthy
\n
Faulty
\n
Healthy
\n
Faulty
\n
Healthy
\n
Faulty
\n
\n\n\n
\n
Rs
\n
4.73E−06
\n
8.02E−06
\n
8.66E−04
\n
9.65E−04
\n
1.11E−07
\n
1.27E−07
\n
\n
\n
Rr
\n
1.77E−09
\n
1.79E−05
\n
9.36E−05
\n
1.14E−04
\n
5.71E−08
\n
7.33E−08
\n
\n
\n
Φds
\n
5.70E−04
\n
6.31E−04
\n
9.02E−05
\n
9.23E−05
\n
21.0E−04
\n
11.0E−04
\n
\n
\n
Φqs
\n
1.93E−06
\n
2.10E−06
\n
1.10E−16
\n
1.10E−16
\n
39.0E−04
\n
27.0E−04
\n
\n
\n
Φdr
\n
2.08E−08
\n
16.00E−04
\n
6.47E−06
\n
6.87E−06
\n
246E−04
\n
210E−04
\n
\n
\n
Φqr
\n
1.73E−10
\n
4.81E−06
\n
1.10E−06
\n
1.10E−06
\n
67.0E−04
\n
70.0E −04
\n
\n\n
Table 8.
MSE values of nonlinear observers: HGO, UKF and MHE are compared.
\n
To verify the robustness, we have performed parametric variation on the observer in relation to the identified values. Figures 7 and 8 show the responses obtained when a rotor inductance variation of +50 and −50% is considered for the observer test. The robustness of the observers’ scheme with respect to this parameter changes is clearly shown. In Figures 7 and 8, it is clearly shown that a +50 and −50% rotor inductance variation generates a high statistical difference on rotor and stator resistances for the unscented Kalman filter. For the high gain observer, that variation is much more felt on the rotor resistance on the both figures. Incontestably the moving horizon estimation seems remain insensitive to the parametric variations but it is not so, it is just that the statistical difference generated is weak enough compared to others. From these responses, we can conclude that the rotor inductance changes do not affect the performance of the moving horizon estimation considerably, but in regard to the other observers, the changes disturb their performance a lot as shown in the figures and that the MHE scheme is robust enough under parametric uncertainties.
In this chapter, a general framework for the doubly fed induction generator has been presented in order to carry out a dynamic estimation of states and parameters of the DFIG. The DFIG parameters are largely influenced by different factors (for instance, temperature, magnetic saturation and eddy current) that is why it is necessary to develop techniques to estimate the changes of parameters. The proposed techniques are performed with high gain observer (HGO), unscented Kalman filter (UKF) and moving horizon estimation algorithms using noisy measurements. A comparison of the three estimation techniques has been made under different aspects notably, computation time and estimation accuracy, in two modes of operation of the DFIG, the healthy mode and the faulty mode. The MHE estimation technique has significantly lower estimation error and converges with fewer samples time than the HGO and the UKF. Whatever the mode of functioning, the simulation results showed that a good standard of performance could be obtained even in the presence of measurement noise.
\n
\n\n',keywords:"doubly-fed induction generator, high gain observer, unscented Kalman filter, moving horizon estimation, parameters estimation, monitoring",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/67365.pdf",chapterXML:"https://mts.intechopen.com/source/xml/67365.xml",downloadPdfUrl:"/chapter/pdf-download/67365",previewPdfUrl:"/chapter/pdf-preview/67365",totalDownloads:57,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 7th 2018",dateReviewed:"January 30th 2019",datePrePublished:"May 27th 2019",datePublished:null,readingETA:"0",abstract:"This chapter presents a general framework for the doubly fed induction generator (DFIG). We apply and analyze the behavior of three estimation techniques, which are the unscented Kalman filter (UKF), the high gain observer (HGO) and the moving horizon estimation (MHE). These estimations are used for parameters estimation of the doubly fed induction generator (DFIG) driven by wind turbine. A comparison of those techniques has been made under different aspects notably, computation time and estimation accuracy in two modes of operation of the DFIG, the healthy mode and the faulty mode. The performance of the MHE has been clearly superior to other estimators during our experiments. These estimation tools can be used for monitoring purposes.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/67365",risUrl:"/chapter/ris/67365",signatures:"Steve Alan Talla Ouambo, Alexandre Teplaira Boum and Adolphe Moukengue Imano",book:{id:"7636",title:"Wind Solar Hybrid Renewable Energy System",subtitle:null,fullTitle:"Wind Solar Hybrid Renewable Energy System",slug:null,publishedDate:null,bookSignature:"Dr. Kenneth Eloghene Okedu, Dr. Ahmed Tahour and Prof. Abdel Ghani Aissaoui",coverURL:"https://cdn.intechopen.com/books/images_new/7636.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"172580",title:"Dr.",name:"Kenneth Eloghene",middleName:null,surname:"Okedu",slug:"kenneth-eloghene-okedu",fullName:"Kenneth Eloghene Okedu"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Mathematical model for DFIG",level:"1"},{id:"sec_2_2",title:"2.1. Modeling of the wind turbine",level:"2"},{id:"sec_3_2",title:"2.2. Modeling of the asynchronous generator",level:"2"},{id:"sec_5",title:"3. Estimation algorithms",level:"1"},{id:"sec_5_2",title:"3.1. High gain observer",level:"2"},{id:"sec_6_2",title:"3.2. The unscented Kalman filter",level:"2"},{id:"sec_7_2",title:"3.3. The moving horizon estimation",level:"2"},{id:"sec_9",title:"4. Numerical results",level:"1"},{id:"sec_10",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Tazil M, Kumar V, Bansal RC, Kong S, Dong ZY, Freitas W, et al. Three-phase doubly fed induction generators: An overview. IET Electric Power Applications. 2010;4(2):75-89\n'},{id:"B2",body:'Marinelli M, Morini A, Pitto A, Federico S. Modeling of doubly fed induction generator (DFIG) equipped wind turbine for dynamic studies. In: 2008 43rd International Universities Power Engineering Conference. Padova, Italy: IEEE; 2008. pp. 1-6\n'},{id:"B3",body:'Shenglong Y, Emami K, Fernando T, Herbert HCI, Wong KP. State estimation of doubly fed induction generator wind turbine in complex power systems. IEEE Transactions on Power Systems. 2016;31(6):4935-4944\n'},{id:"B4",body:'Pena R, Clare JC, Asher GM. Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation. IEE Proceedings-Electric Power Applications. 1996;143(3):231-241\n'},{id:"B5",body:'Ledesma P, Usaola J. Doubly fed induction generator model for transient stability analysis. IEEE Transactions on Energy Conversion. 2005;20(2):388-397\n'},{id:"B6",body:'Rothenhagen K, Fuchs FW. Current sensor fault detection and reconfiguration for a doubly fed induction generator. In: 2007 IEEE Power Electronics Specialists Conference. Orlando: IEEE; 2007. pp. 2732-2738\n'},{id:"B7",body:'Campos-Delgado DU, Espinoza-Trejo DR, Palacios E. Fault-tolerant control in variable speed drives: A survey. IET Electric Power Applications. 2008;2(2):121-134\n'},{id:"B8",body:'Abdelmalek S, Rezazi S, Azar AT. Sensor faults detection and estimation for a dfig equipped wind turbine. Energy Procedia. 2017;139:3-9\n'},{id:"B9",body:'Rothenhagen K, Fuchs FW. Model-based fault detection of gain and offset faults in doubly fed induction generators. In: 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. Cargese: IEEE; 2009. pp. 1-6\n'},{id:"B10",body:'Li D, Lin X, Hu S, Kang Y. An adaptive estimation method for parameters of doubly-fed induction generators (DFIG) in wind power controller. In: 2010 Asia-Pacific Power and Energy Engineering Conference. Chengdu: IEEE; 2010. pp. 1-4\n'},{id:"B11",body:'Julier SJ, Jeffrey KU. New extension of the Kalman filter to nonlinear systems. In: Signal Processing, Sensor Fusion, and Target Recognition VI. Vol. 3068. International Society for Optics and Photonics; 1997. pp. 182-194\n'},{id:"B12",body:'Wan EA, Van Der Merwe R. The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium. IEEE; 2000. pp. 153-158\n'},{id:"B13",body:'Dida A, Attous DB. Doubly-fed induction generator drive based WECS using fuzzy logic controller. Frontiers in Energy. 2015;9(3):272-281\n'},{id:"B14",body:'Pan X, Ju P, Wu F, Jin Y. Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator. Frontiers of Mechanical Engineering. 2017;12(3):367-376\n'},{id:"B15",body:'Heier S. Grid integration of wind energy: onshore and offshore conversion systems. John Wiley & Sons; 2014. pp. 31-117\n'},{id:"B16",body:'Leonhard W. Controlled ac drives, a successful transition from ideas to industrial practice. Control Engineering Practice. 1996;4(7):897-908\n'},{id:"B17",body:'Yang L, Xu Z, Østergaard J, Dong ZY, Wong KP, Ma X. Oscillatory stability and eigenvalue sensitivity analysis of a DFIG wind turbine system. IEEE Transactions on Energy Conversion. 2011;26(1):328-339\n'},{id:"B18",body:'Boum AT, Talla SA. High gain observer and moving horizon estimation for parameters estimation and fault detection of an induction machine: A comparative study. Journal of Control and Instrumentation. 2017;15-26(08):8\n'},{id:"B19",body:'Nijmeijer H, Fossen TI. New Directions in Nonlinear Observer Design. Vol. 244. Springer; 1999\n'},{id:"B20",body:'Bornard G, Hammouri H. A high gain observer for a class of uniformly observable systems. In: Proceedings of the 30th IEEE Conference on Decision and Control. Brighton, UK: IEEE; 1991. pp. 1494-1496\n'},{id:"B21",body:'Rhudy M, Gu Y. Understanding nonlinear Kalman filters. Part II: An implementation guide. Interactive Robotics Letters; 2013. p. 1-18\n'},{id:"B22",body:'Corriou J-P. Process Control. Springer-Verlag; 2004\n'},{id:"B23",body:'Michalska H, Mayne DQ. Moving horizon observers and observer-based control. IEEE Transactions on Automatic Control. 1995;40(6):995-1006\n'},{id:"B24",body:'Rao CV, Rawlings JB, Lee JH. Constrained linear state estimation—A moving horizon approach. Automatica. 2001;37(10):1619-1628\n'},{id:"B25",body:'Robertson DG, Lee JH, Rawlings JB. A moving horizon-based approach for least-squares estimation. AICHE Journal. 1996;42(8):2209-2224\n'},{id:"B26",body:'Slotine J-JE, Hedrick JK, Misawa EA. On sliding observers for nonlinear systems. Journal of Dynamic Systems, Measurement, and Control. 1987;109(3):245-252\n'},{id:"B27",body:'Gauthier JP, Hammouri H, Othman S. A simple observer for nonlinear systems applications to bioreactors. IEEE Transactions on Automatic Control. 1992;37(6):I875\n'},{id:"B28",body:'Choqueuse V, Benbouzid M. Induction machine faults detection using stator current parametric spectral estimation. Mechanical Systems and Signal Processing. 2015;52:447-464\n'},{id:"B29",body:'Liu K, Zhu ZQ. Position offset-based parameter estimation for permanent magnet synchronous machines under variable speed control. IEEE Transactions on Power Electronics. 2015;30(6):3438-3446\n'},{id:"B30",body:'Smith AN, Gadoue SM, Finch JW. Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives. IEEE Transactions on Energy Conversion. 2016;31(1):270-282\n'},{id:"B31",body:'Alonge F, Cirrincione M, Pucci M, Sferlazza A. Input-output feedback linearization control with on-line MRAS-based inductor resistance estimation of linear induction motors including the dynamic end effects. IEEE Transactions on Industry Applications. 2016;52(1):254-266\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Steve Alan Talla Ouambo",address:null,affiliation:'
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