Criterion for new threshold selection [18].
\r\n\t
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His fields of interest are intensive cardiac care, CPR, post-cardiac arrest care, device therapy of arrhythmias, defibrillator waveform, and AED development.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"201263",title:"Dr.",name:"Endre",middleName:null,surname:"Zima",slug:"endre-zima",fullName:"Endre Zima",profilePictureURL:"https://mts.intechopen.com/storage/users/201263/images/system/201263.jpg",biography:"Prof. Dr. Endre Zima works as the chief of Cardiac ICU at Semmelweis University Heart and Vascular Center. Dr. Zima is specialized in anesthesiology-intensive care and cardiology. He has authored 13 book chapters and more than 130 journal papers, achieved a Hirsch-index of 14, g-index of 22, and more than 650 independent citations. \nHe has been holding graduate and postraduate lectures and practices in anesthesiology since 2006, and in cardiology since 2008. He is a PhD Lecturer in Semmelweis University since 2010. He obtains an accreditation of EHRA on Cardiac Pacing and Implantable Cardioverter Defibrillators, he is accredited AALS Instructor of European Resuscitation Council. \nHe is a Fellow of the European Society of Cardiology, member of the European Heart Rhythm Association and Acute Cardiovascular Care Association, board member of the Hungarian Society of Cardiology (HSC), president of Working Group (WG) on Cardiac Pacing of HSC , board member of WG of Heart Failure. Dr. Zima is also a member the Hungarian Society of Resuscitation, Hungarian Society of Anesthesiology. 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It is a renewable resource that will never get exhausted. This energy if properly utilized can lead to greener and safer energy generation compared to coal generated electricity. It is also one of the lowest priced renewable energy technologies available nowadays [1].
In 2015, energy produced in the United States was about 91% of U.S. energy consumption due to less import of petroleum [2]. Majority of energy production being due to fossil fuels, i.e., coal, petroleum, and natural gas. According to Ref. [2], natural gas contributed 32% of total generation, petroleum 28%, coal 21%, renewable energy 11%, and nuclear electric power 9%. However, using natural gas for energy generation has several issues. First, leakage of methane during drilling and extraction of natural gas from wells and its transportation in pipelines [3]. Methane is stronger than CO2 at trapping heat and causing global warming. Methane emissions range from 1 to 9% of total life cycle emissions. Natural gas-fired power plants contribute to acid rain and ground-level ozone, both of which can damage forests and agricultural crops [4].
The present renewable energy-based generation plants such as offshore wind farms are not entirely capable of fulfilling the future needs of the society. Due to this reason, wind-based energy generation is still not very popular and is unable to replace coal or natural gas-based energy production. The monitoring and control systems used are now obsolete and new methods are required.
The control and maintenance actions require complete human interference, and it is a time-consuming process. These challenges lead to extra cost on emergency maintenance, component screening, and physical designs.
Wind turbines consist of several components and are subject to various failures of electrical and mechanical nature [5], e.g., imbalance in electrical controls, gearbox, and yaw system. Some are more frequent and cause larger downtime of the whole system. These faults cause rotor imbalance, unbalances and harmonics in air gap flux, increase torque pulsation, and increase losses and reduction in efficiency by directly affecting the power, current, and voltage output of the generator. Therefore, monitoring of these critical components should be on the highest priority so that plant downtime can be reduced. The offshore located wind turbine generator system requires monitoring of parameters such as sea-surface temperature, wind velocity, water salinity, wave heights, and strain measurement [6, 7]. However, the monitoring of wind turbine parts has several practical difficulties, e.g., limited accessibility, large size and complex geometry of the blades, effect of environmental parameters, etc.
Several papers have discussed methods to detect faults in wind farms, e.g., gearbox fault detection using discrete wavelet transformation [8]. Similarly, high frequency vibration data collected from gearbox testing were used to gearbox fault detection in Ref. [9], which included k-means clustering algorithm. The drawbacks of this system are the assumption that the underlying process is stationary and the time factor is eliminated. Brandão et al. [10] discuss neural networks for fault forecasting of wind turbine gearbox. Badihi [11] presents protection of against the decreased power generation caused by turbine blade erosion and debris on the blades. A fault diagnosis method based on signal analysis and recognition is presented [12]. Time-frequency representations have been proposed in the literature [13–15]. These techniques have high complexity and poor resolution [16]. One approach used Hilbert transformation in a doubly fed induction generator-based wind turbine [17].
Hence, there should be some automated systems to remotely monitor these parameters and notify about faults in the system. By using wireless sensor networks (WSNs), we can ensure reliable operation of wind farm. This helps in reducing manual interference and wind farm can be completely monitored for 24 hours every day. The following sections discuss how this can be performed.
In the past, the monitoring systems used constant threshold to record the data independent of time of the day or month. The constant threshold is calculated as the average of the dataset. As a result of several observations, it can be concluded that such a scheme does not give accurate results if there are changes in the scene or environment pertaining to parameters under consideration, e.g., the temperature of air during daytime is higher compared to night time. Similar variation is true during different seasons, e.g., average temperature during winter season is different from the average temperature during summer season. Hence, if constant or fixed threshold value is chosen for the entire dataset, it is likely to give unoptimized results for both the scenes. Moreover, if the chosen fixed threshold value is very high, it will result in many missed detections, and if it is very low, it will lead to many false positives.
Hence, threshold value should be selected using an appropriate scheme that allows dynamic change in the threshold value to accommodate the variations in time of data recording. This method gives better performance in terms of sensed parameters. The threshold provides a reference for finding values that are higher or lower than the threshold both of which may indicate health failures in the wind farm.
The WSN topology in wind farm consists of tower fixed nodes [18]. These are wireless sensor nodes attached to the tower nodes that can continuously sense the parameter values (samples) throughout the day and night. This information is converted into data packets that are transmitted to the sink node by taking multiple hops through the scattered sensor nodes. The sink node is located at the end of the wind farm. Every tower-fixed node is allocated a fixed local unique address called as RTN id (row-tower-node), which is transmitted as an identification of the originator of packet.
Suppose
and
The samples collected during the night period.
The decision of choosing a new threshold for the dataset depends on the correlation between the datasets. The correlation is the measure of the similarity content between the two datasets. If the correlation of the two datasets is high, it means that the two datasets correspond to the similar time duration of the collected data and hence eliminate the need for calculating another threshold for the new dataset. Similarly, low correlation is indicative of large variations and necessitates the calculation of new thresholds for better data interpretation.
The correlation between the two datasets
where
Figure 1 shows the scatter plot for wind speed dataset and its computed correlation coefficient. Table 1 shows the degree of similarity between the datasets depending on the calculated correlation coefficients.
Scatter plots wind speed. Corr_coeff = −0.07846169 [18].
Sl. No. | Corr_coeff. | Degree of similarity | New threshold required |
---|---|---|---|
1 | −0.7 to +0.7 | Low | Yes |
2 | Less than −0.71 | High | No |
3 | More than +0.71 | High | No |
Criterion for new threshold selection [18].
To calculate the thresholds,
We consider geometric mean because the datasets are characterized by a majority of similar values and relatively very few extreme values. Being strongly influenced by few extreme observations, the threshold values calculation based on arithmetic means would fail to provide a real means of identifying the extreme values.
The use of a geometric mean normalizes the range being averaged, so that no range dominates the weighting, and a given percentage change in any of the properties has the same effect on the geometric mean. Table 2 shows the calculated threshold values for the flexible threshold method and the mean method (MM).
Furthermore, the method requires ranging of the infinite sample values into discrete levels without changing the meaning of information using quantization. To do this, first, the distance matrices
SRC | Variation range | Wind speed | ||||
---|---|---|---|---|---|---|
Range values | Corr. coeff | FTS (TFTS) | MM (TMM) | TFTS − TMM | ||
1(X) | (Min) | 0.4000 | −0.0784 | 10.6938 | 5.9778 | 4.7160 |
(Max) | 12.6000 | |||||
2(Y) | (Min) | 0.1000 | 12.6125 | 6.6347 | ||
(Max) | 14.4000 |
Threshold values [18].
where these matrices represent the values of
Finally, the quantization is performed on the above values independently with respect to their maximum and minimum values. This can be expressed as
where Q is the number of quantization levels for distance matrix d.
The present scenario considers five quantization levels 0, 1, 2, 3, and 4 calculated as from Eq. (10). If the variation in the datasets and the total number of samples in it is large, the number of levels may also increase for better accuracy in fault prediction. This would lead to increase in the size of transmitted packets because the number of bits required to encode each level into binary will also increase. This would cause greater energy depletion in packet transmission, reception by the sensor nodes thus lowering the WSN network lifetime. Thus, the choice of the number of quantization levels should be able to provide accurate fault prediction without compromising the network lifetime. Each level carries a significant and distinct meaning regarding the sensed value, e.g., level “0” indicates that there is no difference between the sample value and the threshold. Similarly, levels “2,” “3,” and “4” indicate increased levels of variation. Figure 2 depicts the above method.
Comparison between MM and flexible method for monitored parameter wind speed [19, 20]. (i) Collected samples of maximum and minimum wave heights, (ii) quantized levels using the FTS method, and (iii) quantized levels using the MM method.
The flexible threshold selection (FTS) method is compared with the mean method (MM). We have considered a total of 72 samples collected during daytime and nighttime for wind speed. The sampling frequency is 1 sample per 10 minutes over a period of 12 hours daytime and 12 hours nighttime. As observed by the simulation results, the flexible threshold method gives a better performance and accurate results for parameter monitoring. Table 2 depicts the range of collected samples and their calculated thresholds using flexible threshold selection (FTS) and mean method (MM). As observed, the datasets for source 1(X) and source 2(Y) have small variations. If both the datasets from the sources are instead, considered to be one single dataset, the variation of values is large. This causes the static threshold selected using the MM method tends to be biased toward the higher values. However, this is not the case with dynamic threshold. We can calculate different thresholds for datasets collected at different times, which will adapt with the true variations of the values known to nature. Hence, the flexible method is unbiased toward any extreme values and gives a balanced view of the data under consideration. The MM method does not consider computing new threshold every time but it remains unchanged for any dataset making it an unrealistic choice.
Two different thresholds for both the sources find the correlation between them by considering them individually.
It is clear from the above discussion that the choice of appropriate threshold has a large impact on the quantization levels. The MM method for threshold selection is only able to detect large variation in the values, i.e., levels “2” and “3” whereas in the FTS method the detected levels have a distributed pattern, i.e., it can detect both small and large variations. Also, the levels detected by the FTS method is consistent compared to the MM method, which provides a very accurate status of the conditions of the wind farm.
Figure 2 shows that the FTS method suggests a majority of level “0” occurrences over other levels unlike the MM method where the majority is level “2” occurrences. Thus, it can be concluded that the flexible method is unbiased toward the larger values in the datasets and hence provides better accuracy of monitored parameters. The graph in Figure 2 is generated from real-time data from the Burbon-Nysted wind farm, Denmark.
This system is called a fuzzy inference system (FIS)-based fault detection method (FFDS). This is an automated system, gives precise information of the health condition of the wind farm to the remote observer, and gives alarms for taking corrective or preventive measures for maintaining the reliability of the farm.
The observer needs not observe all the properties of the parameter values as a single signal, rather, the degree of similarity between the values finds the basis for choosing a new threshold. This is a simple method that helps in finding real-time data for monitoring purposes. These data when analyzed can predict all possible fault occurrences.
The fault detection scheme uses combination-summation (CS) and flow directions (FDs) to design the FIS [9]. This aids to derive significant information from the quantized levels about fault event occurrence in the monitored data samples of offshore wind farm. The received quantized levels corresponding to monitored data samples represent the surrounding environmental conditions in offshore wind farm. The received values being fuzzy in form use FIS to provide accurate interpretation of the environmental conditions.
For determining the CS and FD, five consecutive received levels are considered in one period of time “T” where
which represents five consecutive time intervals. Depending on the permutation and the summation of the levels, fuzzy logic is used to predict fault occurrences. For example, consider the levels received at “t” times are
where the range of CS is [0–20]. The numeral 20 indicates constant occurrence of level 4, i.e., 44,444. The obtained levels can be either repeating or nonrepeating, e.g., 22,222, 31,224, 01,234, and 43,210 as depicted in Figure 3. The CS for these levels is 10, 12, 10, and 10. As observed, this alone is not sufficient for fault prediction. Fault prediction can give accurate results if the corresponding FD is also considered with the values. Here, FD means whether the received levels are in state of increasing, decreasing, remaining stable, or varying constantly. For example, if the CS is 10, it has multiple values, but if FD has raising edge, it means the combination suggests fault event occurrence in the future and calls for immediate preventive action. If the levels increase constantly, then FD is considered to be raising edge shown by arrow in upward direction (Figure 3).
Illustrations of repeating-level and nonrepeating-level combinations. (a) Stable flow, (b) average flow, and (c) raising/dropping flow.
The remote observer is able to predict meaningful information from these received quantized levels based on the fuzzy-logic rules as presented in Table 3.
Sl. No. | Flow directions | Combination-summations | Result (MF) |
---|---|---|---|
1 | Stable (S) | 0, 5 | Normal |
2 | Stable | 10 | Low risk |
3 | Stable | 15 | High risk |
4 | Stable | 20 | Very high risk |
5 | Rising (R) | 10 | High risk |
6 | Dropping (D) | 10 | Low risk |
7 | Average (A) | 1–4 | Normal |
8 | Average | 5–6 | Low risk |
9 | Average | 7–9 | High risk |
10 | Average | ≥ 10 | Very high risk |
Fuzzy rule base.
In this system, the fuzzy-set “F” can be described [7, 8] as
where ω is the combination-summation and flow-directions of the received levels by the remote observer, m(ω) is the membership function for the received level and U is the universal set representing the set of all levels, as shown in Table 3. The membership function alerts the remote observer whenever the probability of fault occurrence becomes high (Figure 4). It can be formulated with risk Ri as
Flowchart to depict working of fuzzy inference system.
This method is very simple to implement and efficient in enhancing the WSN lifetime.
Table 4 shows the comparison for wind speed data computed from Figure 2. The results confirm the belief that FFDS is able to predict accurate conditions of the wind farm. As shown, it predicts normal operation of the farm, whereas MM is only able to detect extreme values of level “0” and “3.” This leads to false alarm for corrective measures due to inaccurate calculations. Thus, it can be concluded that the FTS method is unbiased toward the larger values in the datasets and hence provides better accuracy of monitored parameters.
Levels | Number of quantized levels | Number of consistent quantized levels | ||||
---|---|---|---|---|---|---|
FTS | MM | FTS | prediction | MM | Prediction | |
“0” | 136 | 19 | 32 | Normal operation | 1 | Fault has occurred (corrective measures) |
“1” | 5 | 37 | 0 | 3 | ||
“2” | 2 | 37 | 0 | 3 | ||
“3” | 1 | 51 | 0 | 9 |
Wind speed [19].
Table 5 provides the details of simulation parameters used in the study. The sink node is located at the farthest point in the field. Figure 5 shows the round in which all the nodes in the area become dead (network-lifetime).
Sl. No. | Parameter | Value |
---|---|---|
1. | Area size | 1000 m × 1000 m, 2000 m × 2500 m, 300 m × 4500 m, 4000 m × 6000 m |
2. | Total number of nodes | 416–1216 |
3. | Total number of fixed nodes | 216 |
4. | Total number scattered nodes | 200–1000 |
5. | Total number of turbines | 72 |
Simulation parameters.
Round in which all the nodes become dead [21].
The method FTS gives an accurate view of the parameter values in real time and the threshold selection does not indicate any biasing toward a particular value, which is confirmed from Table 4. Also, as observed from Figure 5, the network lifetime of WSN network is also increased by nearly 10 times with a packet size of 23 bits.
These observations conclude that the flexible threshold selection method improves WSN network-lifetime by increasing energy savings with respect to earlier methods. Moreover, it is suitable for automated monitoring for all area sizes, large number of nodes and if amount of information to be transmitted is large.
This chapter discusses the flexible threshold selection method for efficient environment monitoring of the offshore wind farm. It uses degree of similarity between the previous and the current datasets for calculating geometric mean-based flexible threshold as it does not get biased due to extreme values in the datasets. The method is compared with the static threshold mean method of threshold selection and the performance is seen to be enhanced.
Also, the automated fault detection method is presented in this chapter. This is a simple method that uses small integer values for indicating faulty conditions of the wind farm in real time. The method uses fuzzy inference system that takes integer values as input and gives output in the form of fault status prediction of the farm. Based on these predictions, the system suggests corrective or preventive measures. The method is proved to be very accurate in predicting the fault condition based on sensed parameter values. In addition, this method allows reduction in the size of the transmitted data packets to 23 bits, which help in increasing the overall network lifetime of the WSN system deployed in the wind farm.
I would like to thank Amity University, Lucknow, for providing me the means and resources in writing this chapter. I sincerely thank our Pro Vice Chancellor and Director ASET, Lucknow campus..
The 21st century is, according to Dede [1], quite different from the 20th in regard to the skills people need for work, citizenship, and self-actualisation. Proficiency in the 21st century differs primarily due to the emergence of sophisticated information and communication technologies (ICTs). All over the world, ICT in education has been incorporated into formal national guidelines of the degree requirements of teacher education as an official policy. Digital technology in itself is often seen as a catalyst for educational change, and technology as a symbol for change is often understood as something positive, as investments in technology supports development in society [2].
\nDespite the fact that a fifth of the 21st century is behind us, it seems we are not up to speed regarding the skills anticipated as central for our digital era. Furthermore, there is a lack of clarity regarding what 21st century skills really are. The digital revolution is part of the change making 21st century skills different from those learned in schooling through the 20th century. ICT is changing the nature of perennial skills that are valuable in the modern world, as well as creating new contextual skills necessary for digital societies [1]. The world has changed fundamentally in the last few decades, and in effect, the role of learning and education has changed. Many of the skills needed in past centuries, such as critical thinking and problem solving, are, according to Trilling and Fadel [3], even more relevant today. How these skills are learned and practiced in everyday life in the 21st century though, is rapidly shifting.
\nThis chapter presents a critical perspective on how learners’ information, media and technology skills can be understood, and how they are connected to learning and innovation skills. Data for this chapter is based on qualitative in-depth interviews of ten teaching educators at the University of Waikato in New Zealand and ten teaching educators from UiT, the Arctic University of Norway. Both countries are facing similar educational challenges when teaching in digital environments, as both must educate teaching students in digital-rich environments with high access to various ICTs and educational resources at home [4]. The universities are similar in size and student numbers.
\nThis comparative study of Norwegian and New Zealand teaching education has led us to question how we educate students to meet the future and whether the educational systems are adapting sufficiently to new digital learning contexts. Is teaching students’ deep learning and critical thinking at risk of being limited in digital learning environments? In short, are students sufficiently prepared for the future?
\nThere is widespread agreement among educators and the public about the importance of the traditional fundamental building blocks that underpin student learning. These skills are often referred to as the 3Rs—reading, writing and arithmetic [5]. These are important skills, but as Crockett et al. [6] have argued, for students to progress from the foundations of learning, teachers need to expand their thinking outside their ‘primary focus and fixation on the Three Rs (3Rs)—beyond traditional literacy to an additional set of 21st century fluencies, skills that reflect the times we live in’.
\nThe notion that the 3Rs are not sufficient when preparing students for the future is not a new idea. Broader skills are needed and have been discussed since the first half of the 20th century. One example is an informal meeting of college examiners attending the 1948 American Psychological Association Convention in Boston, which was the start of the development of the theoretical framework known as Bloom’s taxonomy. This is a well-known and commonly used system of classifying the goals of the educational process beyond the 3Rs [7]. A common ground in the search for 21st century skills is by Keane, Keane and Blicbau [8] described as the 4Cs:
Critical thinking
Communication
Collaboration
Creativity
This understanding is based on three influential organisations associated with education, management, and industry developed definitions for 21st century learning. These organisations are the Ministerial Council for Education, Employment, Training and Youth Affairs (MCEETYA), the American Management Association (AMA), and AT21CS, a public and private partnership among governments, educators, academics, and industries [8]. While basic skills such as numeracy and literacy remain essential building blocks for learning, higher order skills such as the 4Cs are equally vital for learning and employment in the 21st century. Keane and Blicbau [5] write that 21st century skills are about fusing the 3Rs and the 4Cs, but the contextual aspect is also of great importance because context contributes to defining and affecting how different skills are used.
\nStudents in the 21st century live in a technology- and media-rich environment with access to a wide range of information, powerful digital tools, and the ability to collaborate and communicate with others. This affects what form of critical thinking is required. Fundamental to the development of 21st century skills is the importance of ICT for learning [8]. A discussion paper prepared for the European Union stated that information and communication technology (ICT) is at the core of 21st century skills. It is regarded as both an argument for the need for these skills, and a tool that can support the acquisition and assessment of them. The rapid development of ICT also requires a whole new set of competences related to ICT and technological literacy [9].
\nKeane, Keane, and Blicbau [8] write that using these technologies in education matter because students need to be prepared this digital world, in which they require a skillset that is broader than the traditional foundations of the 3Rs. Tucker and Courts [10] claim that teachers who mainly concentrate on a fixed curriculum that focuses on learning through repetition and memorisation find it difficult to connect new technologies to the traditional view of classroom learning.
\nTo be effective, teachers and students need to be able to demonstrate both the 3Rs and the 4Cs in relation to an online world. Government policy has been somewhat based on the assumption that access to technology is the key to achieving success. However, simply providing students with digital technology will not lead to development of these skills. How the teacher utilises these devices in the classroom is important for improved student outcomes [5]. Dede [1] claims that we need to move from consensus about the vision of 21st century learning to a thorough understanding of and commitment to the outcomes of 21st century learning. In reality, he claims, the ability to use digital devices in no way means that students know anything about global awareness or health literacy, learning and innovation skills, life and career skills, or even media literacy skills.
\nThere are new skills to master, and they must be understood intertwined with changing contextual skills. Trilling and Fadel [3] have an extended model, where the 4Cs are part of a skillset called learning and innovation skills. They propose two extended sets of skills: information, media and technology skills; and career and life skills (see Table 1).
\n1. Learning and innovation skills | \n2. Information, media, and technology skills | \n3. Career and life skills | \n
---|---|---|
\n
| \n\n
| \n\n
| \n
Three components of 21st century skills [3].
It is important to keep in mind that digital technology in itself is just a tool. Keane and Blicblau [5] state that without an understanding of learning theory, the use of transformative technology may actually be ineffectual. So, to have digital competence for learning, technological skills must be understood intertwined with other sets of skills and knowledge, like learning and innovation skills (the 4Cs).
\nThis has been an ongoing discussion for centuries, and yet it seems like educational practices and systems are having trouble adapting to the espoused learning theories, required formal policy, and understanding of the need for these skills [11]. Keane and Blicbau [5] criticise education for using technology in schools at the enhancement rather than the transformative stage, meaning that tasks could be completed satisfactorily without using technology, and without really changing the task. They claim we need to better provide the appropriate situations that will allow students to develop skills using the 4Cs. Lund [12] claims that schools either lack a view of technology or operate with a view of technology that is at best reductionist. A central control and management mechanism in schools is a standardised test. These tests provide some insight into students’ learning outcomes, but if used unilaterally, may also risk the development of a limited dynamic practice. As Resnick [13] writes, when preparing children for the future, how learning outcomes are assessed must be reconsidered. We need to focus on what is most important for children to learn, not what is easiest to measure and evaluate. The same concern is expressed when discussing digital technology and education. If we are only concerned with measuring the effects of the use of technology, instead of examining how digital technology changes the school culture, we risk cultivating a reductionist approach and ignoring possibilities for innovation [12]. These challenges are not exclusively related to digital practices, as school traditions for learning have in general been criticised for being pacifying. Jordet [14] writes that Norwegian schools are characterised by sedentary activities where the students are placed in the role of passive recipients of handed down knowledge. Such educational practices give students few opportunities to unfold their relational, meaning-seeking, creative, exploratory, and intentional natures. He states that for schools to be able to contribute to children mastering their lives and becoming participants in work and society, the schools’ traditions, thinking, and practices must be changed to better support students’ self-realising and active natures. Oostveen, Oshawa, and Goodman [15] found that meaningful learning is far more likely if new technologies are recognised as providing transformative opportunities.
\nElstad [16] claims that young people born after 1980 have digital capabilities and are therefore regarded as digital natives, in contrast to older teachers who are described as digital immigrants when born earlier than 1980 [17]. Digital immigrants are in governing positions in education, both as policymakers and educators. Could important stakeholders’ lack of digital technology be the reason education is not keeping up to date with new learning theories? Most teaching students referenced in this study were born in 1980 or later and are considered digital natives. Prensky describes digital natives as ‘native speakers of technology, fluent in the digital language of computers, video games, and the internet’ [18]. In this chapter, we present teaching educators’ evaluations of their students and their learning processes. In other words, so-called digital immigrants are evaluating digital natives, but it is not merely their technological skills being evaluated. As mentioned, these skills must be understood as intertwined. Students’ learning and innovation skills, like critical thinking, are intertwined with their information, media, and technology skills, and both sets of skills must be trained. Combined, it creates the need for new contextual skills. Keane, Keane, and Blicblau [8] write that simply using technology does not guarantee that deep learning will occur. The use of technology needs to align and adapt with our knowledge of learning to be able to operate in a transformative space.
\nA study of teaching students and their educators showed that teaching educators scored higher on professional digital competence than their students, but were more critical towards the technology in educational contexts than their students [2]. The differences between teaching educators and teaching students in this study were mostly unrelated to being digital immigrants or natives. They were connected to the complex competence gained through professional practice, regarding the interaction of content knowledge, pedagogical knowledge, and technological knowledge [19].
\nKnowledge of technology is only one critical component of teachers’ use of technology in their practice; they also need to know how to use it for successful integration in teaching and student learning. Being critical is not necessarily about being behind and not up to date, but about taking steps aside to gain a deeper perspective. Successful teaching is not only about finding the right technology, but also the values, norms, and attitudes that reside within the academic staff in teacher training organisations [2].
\nOne group of digital natives is defined as Generation Z. Tucker and Courts describe Generation Z as those who were born after 1990 [10]. This generation is described as ‘technically savvy, well adapted at communicating via the internet, and used to instant action due to the internet technology they have always known’. The traditional education model has, according to Tucker and Courts [10], been slow to adapt to the learning styles of these students, and researchers across the globe seem to agree on this. What seems more unclear is an understanding of what form of adaptation is needed, and how we get there. How do Generation Z’s learning styles and strategies affect learning processes in education?
\nDeep learning involves paying attention to underlying meaning. It is associated with the use of analytic skills, cross-referencing, imaginative reconstruction, and independent thinking. In contrast, surface learning strategies typically place more emphasis on rote learning and simple descriptions [20]. Deep approaches differ from surface approaches, where reproducing knowledge and syllabus-bounded practices is central. A third approach is the strategic approach, which is based on a competitive form of motivation and attempts to maximise academic achievement with minimum effort [21]. One tool for understanding deep learning is Biggs and Collis’ [22] developed structure of observed learning outcomes (SOLO), which form the basis of the SOLO taxonomy. The SOLO taxonomy focuses on the development of surface understanding to deep understanding, with a continuum of complexity and response to learning across the hierarchy of its levels of understanding. The SOLO taxonomy illustrates different levels of understanding:
Prestructural understanding is described as incompetence.
Unistructural understanding where relevant aspects can be identified.
Multistructural understanding where aspects are combined and described.
Relational understanding integrated in multistructural understandings. Being able to analyse, apply, argue, and compare aspects of one’s understanding.
Extended abstract is when the learner is able to create, formulate, generate, hypothesise, reflect, and theorise based on a relational understanding.
The higher the levels of understanding in the SOLO taxonomy, the higher the level of critical thinking, creativity, and communication. Critical thinking is the discipline of actively and skilfully conceptualising, applying, analysing, synthesising, and/or evaluating information gathered from, or generated by observation, experience, reflection, reasoning, or communication [5, 8]. All these aspects are central for 21st century skills and deep learning.
\nWhen teaching educators are asked about students’ learning processes, there is great concern regarding their ability to apply deep learning approaches. This is a complex field with a range of perceptions and understandings. Many of the teaching educators expressed conflicting views, where they addressed challenges and described how digital technology was fostering learning. In this chapter, we focus on the challenges of teaching with digital technology, and not so much on the benefits, which are many.
\nThis study is based on an explanatory sequential design, in which a conducted survey comprises the first phase of a sequence of methods. It is a comparative study involving 64 Norwegian participants from UiT, the Arctic University of Troms, and 44 New Zealand participants from the University of Waikato, with a response rate of 83.8% and 73.4%, respectively. The survey builds on Argyris and Schön’s theory of action [23] and consists of three main constructs: professional digital competence, professional attitudes towards digital technology in education, and professional application of digital tools.
\nBased on their results, ten participants from each university were invited to participate in an in-depth qualitative interview.
\nThe first step in strategically selecting interview participants was to ensure that all participants had high digital competence, with the aim of gathering informed opinions regarding the use of technology in educational contexts. The second step was to select participants within this group of digitally skilled teaching educators based on maximum variation sampling. Maximum variation sampling is a purposeful selection of participants with different perspectives on a phenomenon [24]. As Creswell [24] explains, the maximum variation sampling strategy requires defining a category that produces different responses to paint a varied picture of the participants. The category attitudes towards digital technology was used to select five participants who responded more critically and five participants who responded more positively towards digital technology within each country (Figures 1 and 2).
\nSelection of Norwegian teaching educators.
Selection of New Zealand teaching educators.
A total of 20 semi-structured interviews were conducted to understand and elaborate upon the results of the survey. The transcribed interviews were subsequently analysed using NVivo. One must consider the uncertainty arising when translating from one language to another. The survey, interview guide, and participant statements were translated from Norwegian to English. There are nuances when translating and analysing that may be lost, and these could have influenced the results. An ongoing collaboration with New Zealand researchers throughout the process was very helpful in concept- and language-related clarifications.
\nThis builds on a comparative study, but findings showed that the challenges experienced were evident in both countries. Despite being from different sides of the globe, teaching educators from both Norway and New Zealand expressed a concern regarding students’ learning in digital contexts. Overall, 13 of the 20 interviewed teaching educators expressed a concern regarding students’ deep learning, critical thinking, and source criticism. They link the students’ lack of learning and innovation skills with their level of digital literacy skills (cf. Trilling and Fadels’ model of 21st century skills). If their learning and innovation skills are not high enough, their use of digital technology seems to be at risk of not being used at a transformative level, and in some instances limits the quality of their learning processes.
\nOne of the teaching educators was quite astonished that students could be very technically competent without being able to search the web for knowledge. He explained that he had bachelor students not able to find literature, and when he demonstrated, the students were blown away as if it was magic. The ability to make use of keywords when searching for information and relevant articles was poor among students, he said, and he was surprised by the fact that they were not able to use the knowledge they ought to have attained during their education.
\nAnother teaching educator claimed that the students’ learning approaches were superficial and based on surface learning. She explained the reason was that they had not learned or practiced deep learning processes. ‘When asked to read a text, they do not extract what is important and relevant. They just dutifully read to complete the task’. She said it was fine that they were using Google when studying, but the worry was that the content seemed to move straight from the screen and out of their mouths, bypassing the students’ own relevant reflections. Another teaching educator claimed that there was an evident difference between students who had studied media and communication at the senior level in school and those who had not. They understood that there was quite a lot of work involved in being able to utilise the digital tools in a productive way, while the rest was basing their learning processes on a copy-paste strategy. She explained that students tended to express a strategy of searching for readymade abstracts online. This was very unfortunate because the type of learning we want to promote in our teacher education is largely based on reflection, not just reproduction of readymade connections between levels of understanding.
\nI asked the teaching educators if it was a challenge to get students to engage in deep learning when readymade answers were easily assessable online. The teaching educator replied, ‘Of course’. He explained how he had noticed that students were often using online references instead of the syllabus. ‘It can be the same keywords as is described in our syllabus, but they would rather google it. So, that is when I question what source criticism they have applied to secure their information’. He explained that the students were not concerned with this, and uncritically used this on tests and exams. One critical question to be asked was: When using a traditional lens when assessing the students, what are we measuring as new tools and contexts for learning have transformed learning activities and outcomes? Do we have practices for evaluation that aligns with the new learning activities and intended outcome?
\nThe same teaching educator’s experience with digital tools was that they were not always helpful. Furthermore, he felt it somewhat distorted/disabled the learning processes. This understanding was confirmed by another educator who explained that she thought of digital technology as a detour. ‘Sometimes we use digital technology like PowerPoint, when traditional methods like using a black board can work as a better tool’. She explained that students expressed their preference for educators using PowerPoint, as they found it better not having to write everything down.
\nIn New Zealand, teaching educators were also vocal regarding this challenge. One teaching educator explained how she had noticed that students were increasingly entering search words in Google to access what she referred to as ‘easy takeaway knowledge’. The consequence, she explained, was that the students did not have to engage deeply or really work with the content. ‘Students can access it very easily, and it almost replaces thorough research, like reading academic articles,’ she said. She explained how this availability of a lot of information on the internet undermined students’ capacity to read critically, do research, and read academic journals or chapters. She elaborated that this aspect of availability, quick easy access, was undermining the development of academic capacities and serious research for assignments. A critical selection of information takes more time. ‘You have to actually digest those harder articles, and it seems to me that students read less of those […] even if they use them in their assignment it is superficial.’ Another one supported this perception and explained: ‘the easiness of technology creates a false notion of what learning is about, that you don’t have to work for knowledge. I don’t think that’s true. If you look at anyone who is good at something, they have put in a lot of work and practice. I think digital technologies might be kind of responsible for this notion of learning’.
\nSome research shows that students who often use technology tend to do worse when compared with students who use less of such tools [4, 25, 26, 27]. Mueller and Oppenheimer [28] conducted a study in which they concluded that the use of a laptop negatively affected the students’ test results. They focused on the students’ use of laptops instead of traditional writing during lectures. They argued that note taking by hand calls for different cognitive processes than writing on a laptop. One can write faster on a laptop and take more notes. ‘Although more notes are beneficial, at least to a point, if the notes are taken indiscriminately or by mindlessly transcribing content, as is more likely the case on a laptop than when notes are taken longhand, the benefit disappears’ [28]. Writing by hand is slower, and one cannot take verbatim notes in the same way as with a laptop. Instead, students listen, digest, and summarise so that they can succinctly capture the essence of the information. Taking notes by hand forces the brain to engage in deeper learning, which fosters comprehension and retention [29, 30, 31]. As May points out, ‘even when technology allows us to do more in less time, it does not always foster learning’. This is in line with the teaching educator who claimed that that learning has a tendency to be too easy. When students are copying and pasting from the internet and using digital technology uncritically, they miss out on the constituting process of struggling with individual concepts and developing their 21st century skills, like reflecting, generating, being creative, theorising different concepts, and communicating independent ideas. It seemed like the teaching educators had trouble engaging students in deep learning processes as digital technology created a learning environment that fostered the strategic approach, and they experienced challenges where students attempted to maximise academic achievement with minimum effort. Perhaps they did this unaware of the consequences these approaches could have on their potential learning outcomes.
\nDeep learning strategies cannot be externally imposed and must be interest-led. Interest can be stimulated by placing less emphasis on curriculum content and more on contextual interpretation, in other words, the 4Cs [20]. Learning activities need to be interesting and engaging and allow critical reflection and dialogue with peers and mentors [32].
\nCritical thinking is vital for problem solving, but one teaching educator explained that students’ critical thinking skills were virtually non-existent, and that a lot of effort was put into trying to develop those skills alongside their digital skills. Another explained that as much as digital tools were creating opportunities in teaching, they were also creating challenges. Those challenges were related to teaching students to be critical. When is it useful to use it, and what resources are usable in academic settings?
\n‘The students’ ability to use and utilise digital tools shocks me, because it is very poor. They are consumers; they are not producers. The job we do here is about making them able to become producers as well, so that they can utilise the learning resources available. They need to be prepared better through high school in relation to the critical use of digital tools; there are many who have major shortcomings. I think it has gotten worse really, because it’s like if it’s not on Facebook or Google, then it does not exist. It’s a little scary. It seems that they are becoming less and less aware that it is just a person who has written this, and that information could have been written with underlying agendas. The critical reflections are something we have to work quite a lot with, and more for each new class just the three years I have been here.’ (translated from interview).
\nOne teaching educator related the challenge to the fact that it was very easy to retrieve information, without necessarily understanding what it means. One can just type in a word or look something up, ‘then you just read exactly what comes out, because you typed in a word’. The problem, she explained, was that the students were not able to see the whole picture. It was noticed in their presentation on exams, or in things they wrote, that they did not fully understand the concepts they were writing about. Their presentation was really just reformulation of something copied from the internet, and was not coherent.
\nOne challenge is related to what extent they understand the concepts they are writing about; another is whether the source is trustworthy. The students were warned both in writing and orally, one teaching educator explained, not to use bloggers’ opinions and secondary interpretations as a basis for academic writing. The students still handed in papers with hardly any syllabus literature or academic references. One teaching educator explained that she had been teaching for so long that she remembered well the time when education was much more book centred.
\n‘One had to search for and order different articles at the library, and so on. Now it is all online, and that is great. It makes things easier. From that perspective, the students have accepted the possibilities online, and that is good. Nevertheless, there is a negative side to this. I do not find that students’ source criticism has developed or increased according to this change. For instance, I do not accept references to Wikipedia in my papers, even if there is a lot sensible information written there. I encourage them to start there to get an overview. It can function as a platform for relevant references. But they have to be critical regarding what they are basing their arguments on, and the skills to do this are lacking.’
\nThe same perception is widespread among the New Zealand teaching educators. One explained that one of the things they were focusing on was critical analysis and information literacy. He said, ‘The information is at our fingertips, but we need to really think about when we’re using it and how it’s being used, and be able to seek out robust information for what we need, and understand exactly what we’re using’. Another participant explained that she had noticed that there was an overreliance on inaccurate media rather than knowing that they could go to a particular resource and have more valid information.
\n‘So they can’t make those kind of judgements about what is valid and what isn’t valid to cite, because there’s been no role models for them to look at and learn from. So the whole concept to any kind of academic approach to writing, whether it is through social media or other aspects of writing, is a very big learning curve for them… they struggle.’
\nThe same challenge was exemplified by an interaction with another teaching educator and a student.
\n‘One of my postgraduate students this week wanted to know what I meant by “doing critical review”, which is an instruction for an assignment. And she copied something in, and I said: Where did you get this from? She said: Oh, I got it off Mr. Google, and I’m sort of thinking is this really, you know… This is a postgraduate student who is saying that, and doing that. That is actually pretty problematic. So, you can’t make too many assumptions about where people are at.’
\nShe explained that the biggest challenge was that the students needed to develop their critical perspectives on what they were seeing, and referred to this as ‘very patchy’. She was trying to encourage academic writing, thinking, and discussion, to make students extract knowledge and the underpinning ideas. To ‘have the students in the position where they can tell the good from the bad, the useful from the not so useful information. That has been a problem.’
\nOne teaching educator challenged the notion of students as superficial in their learning because of digital technology; she claimed that the challenge was about the need for a different set of skills.
\n‘I certainly don’t feel that students are more superficial because they\'re using them, or because they can access Wikipedia or… I think they need to learn a different set of skills, but I think that once you have developed those skills, I think you can actually get into deeper learning, and I think digital technology enhances those skills. I think we can be superficial in whatever we do. But, it’s not because of digital technology we become superficial.’
\nBased on what the teaching educators explained, it seems like digital learning environments are enabling advanced multi-structural learning at such a high level that their lack of relational understanding and ability to create extended abstracts have been overlooked. Digital tools make students appear skilled in handling information as they can copy ready-made text online by googling keywords. This apparent skill in writing could be misleading for teachers in their assessment of the student. When students reach higher education, they are perceived as unskilled and uncritical, as higher education reveals a worrying lack of learning strategies that would enable them to reach deeper levels of understanding [22]. It seems that through primary and secondary education, they develop an imbalance between learning and innovation skills, and information, media, and technology skills [3]. Furthermore, this imbalance seems to create an asymmetrical reinforcing effect as digital environments make it easy to present multi-structural understanding at a high level, which can disguise the need to work with students’ ability to think critically, a central part of the higher order of thinking in the SOLO taxonomy.
\nThat ‘everything used to be better’ is a claim made by all generations. One teaching educator pointed out that ‘students in the past have also written things they do not understand themselves. I do not think that is new. Everyone just wants to find the easiest way to a good grade, maybe.’ However, if seeking the easiest way is a fundamental human trait, it is a challenge for teaching and learning now that knowledge is more easily accessible and presented, without engaging critical thinking and deeper cognitive processes. Wajcman [33] states that ‘Rather than simply saving time, technologies change the nature and meaning of tasks and work activities, as well as creating new material and cultural practices’. We need to adapt to these changing practices and learning activities, and adjust how we educate our students to be prepared in this new learning context. The teaching educators in this study had some suggestions.
\nTeaching educators in this study expressed a worry regarding the digital format versus traditional books. As information is more easily accessible, students tend not to read the books and research the greater context information it was gathered from. In a book, you often have to read larger sections to get a grasp of the concepts. When googling keywords, it is easy to find a lot of ‘hits,’ and then mix a selection of copied sections. This can apparently look like a reasonable text, but it is surface learning and without deep understanding of the content. Reading a book will perhaps create deeper learning, even though the text produced is less polished than a copy-paste text from already digested sections online.
\n‘I mean obviously, students have different skills, but I am thinking that critical thinking skills, reading hard information is definitely undermined, that is what I am thinking. I am noticing that with students.’ (New Zealand teaching educator)
\n‘I do not think their digital skills have become any higher in the last five years, I think almost on the contrary. They are very good at watching videos and looking for things online, but I do not think they are good at retrieving relevant information. They are not as source-critical as I would like. We probably have a job to do to make them able and skilled.’ (Norwegian teaching educator)
\nThe two skillsets, learning and innovation skills and digital skills, are connected. Students will not flourish in their digital skills if they are not intertwined with the 4Cs. Digital natives and Generation Z have a good technical understanding, but integrating that with the skills of being creative and critical is central to achieving deep learning processes in digital learning environments.
\n‘They (students) are not able to transfer those skills and understandings into their learning environment. I would say the key thing again here is that the students might come in with skills and abilities, but not necessarily pedagogical understanding of how to actually implement that in their teaching practice. I think that\'s the key thing that we, initial teacher education lecturers, need to really focus on, and I think we need to come up to the plate and think about the digital literacies our students have… and actually think about being responsive to those as well.’ (New Zealand teaching educator)
\nOne teaching educator who perceived students as getting shallower in their learning was vocal about the value of structuring education around the use of books as well as digital devices.
\n‘I require them to read a textbook, because I think that doing lectures actually, online, is actually not a satisfactory way to get one’s point across. So instead, what I do is I weave my points across all the ways that I teach each week, so all the things I present, all of my interactions and discussion groups and… I think it works up to a point, but I\'m expecting them to read the textbook quite well, really.’
\nTo round up this chapter, I leave the final word to one of the New Zealand teaching educators who summed up most of the main findings in our study.
\n‘I think digital technology can be a lot more passive at times, and in terms of students, I think they just see technology as providing the answer. I think it is important to challenge them and say, “There may not be an exact answer to the question; you have to keep challenging and questioning.” I sometimes believe they have become a lot more passive, and just accepting what comes via the technology as being the one and only, or the right way of doing things. Rather than challenging. I think it is due to the way the world has shifted. Where it is a lot easier for them to go online and get something, rather than physically having to go somewhere and think about it, like a library or hunt out a book, or… Everything is right there. Therefore, I think that passive learning most probably happens a lot more because of the technology, because they can just access wherever they are. In terms of preparation, coming through from high school, yes, I think there are some definite skills in terms of being critical of information that needs to be taught, prior to coming into higher education. Particularly in the sense of questioning the information they are accepting. I believe some disadvantages are that most probably the students do not challenge enough, they just accept technology, and I think that might be the way technology has been introduced over the years. “Here it is, here is the answer.” “If you don’t know, just google it, and you’ll get something.” So that passive, not questioning, not challenging… I think is a real disadvantage.’
\nIt seems that students’ development of critical thinking and deep learning is challenged in digital learning environments. A high level of ICT literacy seems to challenge the lens traditionally used to assess students’ capabilities and needs. Furthermore, ICT skills and learning and innovation skills seem to mutually influence each other, as low learning and innovation skills make the students’ ICT skills stagnate when assessing their critical use of online resources. We find that learning in a digital environment complicates the development of critical thinking, but we also believe that this can be corrected by redefining what it takes to prepare students for the future. For a long time, the focus has been on developing their digital skills. However, it would seem like we have not paid enough attention to what the digital transformation requires of interwoven aspects related to learning in digital societies. We need to develop the traditions in education, where the focus has been on technical skills more than on interdisciplinary competencies. If we are able to better secure and develop students’ abilities to be critical and creative, and to collaborate and communicate, digital learning environments could act as learning resources for all students. Without this skillset, there is a risk of students using digital resources in a way that prohibits deep learning and the development of higher order thinking. Based on the input of the teaching educators, it is essential that education is structured in a way that a lack of the 4Cs is noticed by educators and teachers, and that learning is structured to develop such skills. It is unfortunate if students acquire a high degree of information, media, and technology skills, as digital immigrants do, without the learning and innovation skills required to manoeuvre constructively in the overwhelming and easily accessible landscape of digital learning. Education needs to structure learning that challenges students to connect different skillsets, so new contextual skills and knowledge are developed. Just like critical thinking in digital spaces.
\nThe publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway.
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