The main characteristics of the dataset.
\r\n\tThis book will mainly cover work related to: (i) cells mechanosensing and mechanotransduction mechanisms (ii) computational and experimental techniques in mechanobiology, (iii) mathematical mechanobiological models of bone remodeling, (iv) bone mechano-transduction, (v) innovative tools for mechanobiology and the role of medical imaging in this field and (vi) any other proposals related to innovations, clinical application and perspectives of mechanobiology.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"0a38ccecc83b50d8b015a6dd2533049d",bookSignature:"Prof. Abdelwahed Barkaoui",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10255.jpg",keywords:"Nuclear Mechanotransduction, Mechanosensitivity, Fluids Mechanics, Multiscale Mechanobiology, Modeling Cellular Mechanics, Finite Elements Method, Bone Remodeling, Mechanics Stimulus, Multi-scale Modeling, Mechanobiology Tools, Cell Imaging, Cell-Substrate Interactions",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 2nd 2020",dateEndSecondStepPublish:"July 23rd 2020",dateEndThirdStepPublish:"September 21st 2020",dateEndFourthStepPublish:"December 10th 2020",dateEndFifthStepPublish:"February 8th 2021",remainingDaysToSecondStep:"9 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Assistant director of LERMA laboratory, head of mechanical discipline at ECINE and coordinator of the ECINE study program accreditation committee, a member of the editorial board of several international scientific journals, also a member of the American Society of Mechanical (ASME) Engineers European Society of Biomechanics (ESB) and the International Society of Biomechanics (ISB).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"320631",title:"Dr.",name:"Abdelwahed",middleName:null,surname:"Barkaoui",slug:"abdelwahed-barkaoui",fullName:"Abdelwahed Barkaoui",profilePictureURL:"https://mts.intechopen.com/storage/users/320631/images/system/320631.jpg",biography:"Abdelwahed Barkaoui is an associate professor of Mechanical Engineering at International University of Rabat. He obtained his University habilitation from university of Tunis El Manar-Tunisia in 2017 and his PhD from university of Orleans-France 2012. He has a master's degree in mechanics obtained from the INSA of Lyon-France. He held a position of director of the department of sciences and techniques of engineers and was a Member of the Scientific Advisory Board of the Preparatory Institute for Engineering Studies of El Manar. Currently, Dr. Barkaoui is an assistant director of LERMA laboratory, head of mechanical discipline at ECINE and coordinator of the ECINE study program accreditation committee. His research mainly concerns problems in biomechanics, mechanobiology and biomedical engineering. He is a member of the editorial board of several international scientific journals as Series on biomechanics (2019-), Frontiers in Bioengineering and Biotechnology “biomechanics” (2017-), The Open Biomedical Engineering (2019-), EC Orthopedics (2017-) and Reviewer for several international journals known in the field of biomechanics and mechanics (Scientific Reports (nuture.com), Engineering Fracture Mechanics, Biomechanics and Modeling in Mechanobiology, etc). He is also a member of the American Society of Mechanical (ASME) Engineers European Society of Biomechanics (ESB) and the International Society of Biomechanics (ISB). 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From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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There are three types of heat treatment; (1) low temperature long time (LTLT) pasteurization, (2) high temperature short time (HTST) pasteurization, and (3) ultra-high temperature (UHT) treatment. In all types of heat treatment, the Maillard reaction occurs in milk.
The Maillard reaction (nonenzymatic glycation) is a chemical reaction between amino group and carbonyl group; it is the extremely complex reaction that usually takes place during food processing or storage. In the case of milk, lactose reacts with the free amino acid side chains of milk proteins (mainly ε-amino group of lysine residue) to proceed to early, intermediate, and advanced stages of Maillard reaction and forms enormous kinds of Maillard reaction products. The reactions of lactose and milk proteins have been frequently investigated and the formations of various Maillard reaction products in milk during heat treatment have been demonstrated [1]. In the general Maillard reaction, firstly an Amadori product is generated, and it progresses to the 3-deoxyosone or 1-deoxyosone route depending on the reaction pH. In the case of the Maillard reaction of disaccharides such as lactose, there is a third reaction route. It is the 4-deoxyosone route. A main carbohydrate in milk is lactose. Thus, the Maillard reaction in milk progresses via the above described three routes. Finally, the Maillard reaction results in the formation of melanoidins (browning compounds).
The Maillard reaction shows various effects on milk proteins such as bioavailability, solubility, forming property, emulsifying property, and heating stability [1-4]. In addition, the formation of flavor compounds and browning compounds is caused as the consequences of the Maillard reaction between lactose and milk proteins [1, 5].
As for the effect of the Maillard reaction on the bioavailability of milk proteins, various studies were performed. Generally, in the Maillard reaction in milk, lactose mainly reacts with ε-amino group of lysine residue of milk proteins. Thus, the lysine loss by the Maillard reaction increases with a severity of heat treatment. The modified lysine cannot be available as a nutrient any more. For example, steam injection process (direct heating) generated 3.6% (120°C for 400 sec) and 6.8% (130°C for 290 sec) of the blocked lysine in whole milk. The indirect heating at 115°C for 10 to 40 min increased the modified lysine from 11.0 to 13.0% [6]. In addition, it was revealed that the lysine residues in skim milk powder were more susceptible to heating than those in skim milk [7].
Le
The Maillard reaction has a lot of effects on the function of milk proteins and sensory property of milk and dairy products as described above. Particularly, in the manufacturing of milk, the excess progress of Maillard reaction and the formation of melanoidins are undesired, because a commercial value of milk is drastically decreased by them. Therefore, the detection of the Maillard reaction products is important for the quality control of milk. So far, several heat-induced markers have been proposed to control and check the heat treatment given to milk and dairy products. For example, furosine, hydroxymethylfurfural (HMF), and lactulose concentrations have been recognized to be the most promising indicators, since these concentrations increase with the heat treatment [1]. In Japan, protein reducing substance value (PRS) obtained by a ferricyanide assay is also widely-used conventional indicator [8]. It is based on the detection of reducing substances such as sulfhydryl group which are generated by heating in the fraction of acid-precipitated milk protein. These method, however, are generally time-consuming and complicated.
We proposed an assay method for determining the ability of milk to reduce 3’-[1-[(phenylamino)-carbonyl]-3,4-tetrazolium]-bis(4-methoxy-6-nitro)benzensulfonic acid hydrate (XTT: Figure 1) as a method of evaluating the extent of the Maillard reaction [8-11]. The tetrazolium salt XTT is reduced to water-soluble formazan which is suitable for the spectrophotometric measurement. Taking account into an economical view and a rapidity of assay, we tried to develop a microplate assay. The assay conditions were as follows: XTT concentration, 0.5 mM; reaction pH, 7.0; menadione concentration, saturation level (ca. 0.55 mM); reaction temperature, room temperature; volume of XTT solution, 60 μL; volume of sample solution, 40 μL; detection wavelength, 492 nm; reference wavelength, 600 nm; reaction time, 20 min. The procedure of the XTT assay was as follows: the XTT solution was added into each well in a microplate. Afterward, the sample solution was added to the well. After mixing on a microplate shaker at 500 rpm for 15 sec, a difference in the absorbance between 492 and 600 nm was read on a microplate reader as the absorbance at 0 min. After 20 min, the absorbance difference was read again. An increase in the absorbance for 20 min was recorded as the ability of the sample to reduce XTT (XTT reducibility).
Structure of tetrazolium salt XTT.
Using the XTT assay described above, the XTT reducibility of milk was examined (Figure 2). In this test, an LTLT milk (Milk A: 65°C for 30 min) and two kinds of UHT milk (Milk B: 130°C for 2 sec, Milk C: 140°C for 3 sec) were used. When the milk was mixed with XTT solution, intense orange color which was derived from the XTT formazan was recognized. As shown in Figure 2, Milk C showed the highest XTT reducibility and the order of XTT reducibility was Milk C, B, and A. At the same time, the HMF content in Milk B and C was also determined as the conventional indicator of the Maillard reaction, because it was reported that the HMF content clearly increased with the severity of the heating treatment of milk [12, 13]. As a result, the content of HMF in Milk C (12.6 μM) was higher than that of Milk B (8.95 μM). From these results, it was revealed that the XTT assay could estimate the degree of thermal stress delivered to the milk as well as the HMF value.
In addition, the changes in the XTT reducibility during storage of UHT milks (Milk B and C) were investigated. For purpose of comparison, the PRS of Milk B and C were also examined by the ferricyanide assay [8]. The UHT milks were stored at 4°C and 37°C for about 4 weeks (Figure 3, 4). The XTT reducibility of Milk B and C gradually decreased depending on the storage period and the rate of decrease in the XTT reducibility was clearly larger at higher storage temperature (Figure 3). This result strongly suggested that, when the XTT assay is applied to the milk heated under a given condition, the result can serve to estimate not only the heating condition but also the storage period after heat treatment if the storage temperature is known or if storage period is known.
On the other hand, the PRS of Milk B and C were almost constant at all storage conditions (Figure 4). This result showed that the ferricyanide-reducing substances might be stable unlike the XTT-reducing substance. In addition, we found that the HMF value of Milk C did not significantly change during its storage for 60 days at 5°C [11]. It was in accordance with the result of Fink and Kessler about HMF [13]. They also reported that the lactulose concentration of UHT milk was constant throughout the storage period for 70 days at room temperature [13]. It could be concluded, therefore, that the XTT assay is applicable for the estimation of storage conditions which was impossible by the conventional method.
Comparison of XTT reducibility of LTLT milk (Milk A) and UHT milks (Milk B and C).
Changes in XTT reducibility during storage.
Changes in PRS during storage.
In order to clarify the XTT-reducing substance that is formed during the Maillard reaction in milk, we firstly used a model system consisting of lactose and butylamine, and then performed the spectrophotometric analysis of the heated model solution. The model solution of lactose-butylamine heated at 80-100°C for 0-30 min showed a characteristic UV absorption maximum at 320 nm. During the heating at 80-100°C for 30 min, the changes in the absorbance at 320 nm (Figure 5) and the XTT reducibility (Figure 6) were investigated. As a result, both indices increased gradually in accordance with the rise in temperature and heating time. This result indicated that the compound with the absorption maximum at 320 nm was formed by heating. Moreover, the behavior of increase in absorbance at 320 nm was similar to that of the XTT reducibility. Since the similar trend was recognized in the time course of both indices, the XTT reducibility was plotted against the absorbance at 320 nm. In consequence, a significant relationship between them was recognized with a correlation coefficient of 0.967 (n = 19,
The 13C- and 1H-NMR analyses of the compound with the absorption maximum at 320 nm which was extracted from lactose-butylamine model solution heated at 100°C for 15 min were performed. The signals of the 13C- and 1H-NMR could be assigned the compound as the aminoreductone, 1-(butylamino)-1,2-dehydro-1,4-dideoxy 3-hexulose (Figure 7). This compound was reported as the Maillard reaction product formed in the 4-deoxyosone route. It was also reported as the characteristic compound in the Maillard reaction of disaccharides [14]. In addition, we demonstrate a linear relationship between the XTT reducibility and the amount of aminoreductone which was determined more specifically by HPLC [10]. These results strongly indicated that the aminoreductone formed during Maillard reaction of lactose was mainly responsible for the reduction of XTT.
Effect of heating temperature and time on the absorbance at 320 nm.
Effect of heating temperature and time on the XTT reducibility.
Structure of aminoreductone generated by the Maillard reaction of lactose and butylamine.(R = butyl group)
From these results, we presumed that the aminoreductone is formed by the Maillard reaction between lactose and ε-amino groups of milk proteins, and then it is responsible for the reduction of XTT. However, at that time, there was no report to prove the presence of aminoreductone in milk. Thus, we tried to demonstrate it using model system consisting of lactose and milk proteins and UHT milk.
As a model system of milk, the solution consisting of lactose (4.6%) and casein (2.6%), α-lactalbumin (0.12%), or β-lactoglobulin (0.32%) was used and heated at 130°C for 15 min. After heating, the characteristic absorption maximum or shoulder at 320 nm was recognized. In addition, the changes of the absorbance at 320 nm (Figure 8) and the XTT reducibility (Figure 9) were investigated. In all model systems, the increases in the absorbance at 320 nm and the XTT reducibility depended on the heating time. Because similar tendencies were observed between two indices in all model systems, correlations were examined. Consequently, there were significant linearities as follows: casein (r = 0.993, n = 6,
As described above, a possibility of the formation of aminoreductone on the milk proteins during the Maillard reaction with lactose was clearly shown. However, direct demonstration of the presence of aminoreductone in milk had not been accomplished because of a difficulty in isolation of an intact aminoreductone from milk proteins. For instance, aminoreductone is labile and hence not suitable for enzyme hydrolysis and multiple extraction steps. To achieve the practical application of the XTT assay in food industries including dairy products, it was essential to demonstrate the presence of aminoreductone in milk. Because of this background, we attempted to isolate aminoreductone from milk proteins using 2,4-dinitrophenylhydrazine (DNP), a common labeling reagent for the carbonyl group, and Cu2+ [16]. A mechanism of derivatization of aminoreductone in milk is shown in Figure 10. In this derivatization step, Cu2+ plays as an oxidizing agent against aminoreductone, and the oxidized aminoreductone (OAR) has two or three carbonyl groups. Finally, it was assumed that two or three carbonyl groups in OAR are derivatized by DNP (OAR-DNP).
Effect of heating time on the absorbance at 320 nm.
Effect of heating time on the XTT reducibility.
Derivatization mechanism of aminoreductone in milk using DNP and Cu2+.
The derivatization using DNP and Cu2+ was applied to aminoreductone in UHT milk (140°C, 3 sec). In this study, the UHT milk was reheated at 130°C for 15 min in order to increase the content of aminoreductone, because the original content of aminoreductone in the commercially available UHT milk was not so high. As a result, the reheating of UHT milk could increase the content of DNP derivative by 40 times. The DNP derivative which was thought to be corresponding to OAR-DNP in the reheated UHT milk was purified by preparative normal-phase HPLC and preparative reversed-phase HPLC. Finally, the purified compound (4.2 mg) was obtained from 980 mL of UHT milk and analyzed by 13C- and 1H-NMR. The NMR signals of the DNP derivative from UHT milk could be assigned to the structure of OAR-DNP shown in Figure 10. In addition, the NMR signals of DNP derivative from UHT milk were nearly the same as those of the OAR-DNP from lactose-butylamine model system. These results demonstrated that aminoreductone was formed by the Maillard reaction on the milk proteins and present in milk.
Therefore, considering the above, the principle of the present XTT assay can be concluded as follows (Figure 11): (1) Lactose and ε-amino groups of lysine residue in milk proteins react non-enzymatically to form the Amadori product by the heating process. (2) Aminoreductone structure is formed on the milk proteins after elimination of galactose moiety from lactose through 4-deoxyosone pathway. (3) Aminoreductone is oxidized by XTT, whereas XTT is simultaneously reduced to the corresponding water-soluble formazan.
It would be thought that the above mentioned steps (1) and (2) progresses depending on the time and temperature of heating process in milk production, so the XTT assay can differentiate the extent of heat treatment of milk. Based on the study using model system of lactose and butylamine, the relationship between aminoreductone concentration and XTT reducibility was examined. As a result, there was a good linearity was recognized (r = 0.98) and a regression equation was
Principle of XTT assay.
As described above, during the course of elucidation of the XTT-reducing substance, Cu2+ was used as the oxidizing agent (Figure 10), because we empirically knew that it was easily decomposed by the addition of Cu2+ [18]. In fact, the aminoreductone extracted from the heated model solution of lactose and butylamine was rapidly oxidized by the addition of Cu2+, and simultaneously the XTT reducibility was also lost. On the other hand, in our previous work, it was revealed that the commercially available UHT milk contains Cu2+ at the concentration of 30 μg/L [18]. Thus, it was easily presumed that the aminoreductone formed by the heating process was gradually oxidized by endogenous Cu2+ in milk during storage period. This was the reason why that the XTT reducibility decreased depending on the storage period (Figure 3). The detailed investigation about the relationship between the aminoreductone concentration, Cu2+ concentration, and storage stability of milk is now in progress.
The functionalities of aminoreductone have attracted interest and, so far, some studies were performed. Trang
Furthermore, aminoreductone showed antimicrobial activity against
In this chapter, the demonstration of the presence of aminoreductone formed by the Maillard reaction in milk and the specific assay method for aminoreductone were focused and introduced. Since the novel functionality of aminoreductone have come out one after another, the information of aminoreductone obtained by the XTT assay would gain importance in the quality control in milk and dairy products.
Recently, online systems in education have increased, and student digital data has come to big data size. This makes possible to draw rules and predictions about the students by processing educational data with data mining techniques. All kinds of information about the student’s socioeconomic environment, learning environment, or course notes can be used for prediction, which affect the success or failure of a student.
In this study, the successes of the students at the end of the semester are estimated by using the student data obtained from secondary education of two Portuguese schools. The aim of this study is to predict the students’ final grades to support the educators to take precautions for the children at risk. A number of data preprocessing processes were applied to increase the accuracy rate of the prediction model. A wrapper method for feature subset selection was applied to find the optimal subset of features. After that, three popular data mining algorithms (decision tree, random forest, and naive Bayes) were used and compared in terms of classification accuracy rate. In addition, this study also investigates the effects of two different grade categorizations on data mining: five-level grade categorization and binary grade categorization.
The remainder of this paper is organized as follows. In Section 2, the previous studies in this field are mentioned. In Section 3, the methods used in this study are briefly explained to provide a comprehensive understanding of the research concepts. In Section 4, experimental studies are presented with dataset description, data preprocessing, and experimental result subtitles. Finally, conclusion and the direction for future research are given in Section 5.
Predicting students’ academic performance is one of the main topics of educational data mining [1, 2]. With the advancement of technology, technological investments in the field of education have increased. Along with technological developments, e-Learning platforms such as web-based online learning and multimedia technologies have evolved, and both learning costs have decreased, and time and space limitations have been eliminated [3]. The increase of online course trainings and the increase of online transactions and interactive transactions in schools led to the increase of digital data in this field. Costa (2017) emphasized the data about the failure rate of the students; the educators were concerned and raised important questions about the failure prediction [4].
Estimating students’ performances becomes more difficult because of the large volume of data in training databases [5]. Descriptive statistical analysis can be effectively used to provide the basic descriptive information of a given set of data [6]. However, this alone is not always enough. To inform the instructors and students early, students may be able to identify early, using estimated modeling methods [7]. It is useful to classify university students according to their potential academic performance in order to increase success rates and to manage the resources well [8]. The large growth of electronic data from universities leads to an increase in the need to obtain meaningful information from these large amounts of data [9]. By using data mining techniques on education data, it is possible to improve the quality of education processes [10].
Until now, data mining algorithms have been applied on various different educational fields such as engineering education [11], physical education [12], and English language education [13]. Some studies have focused on high school students [14], while some of them have interested in higher education [15]. Whereas some data mining studies have focused on the prediction of student performance [16], some studies have investigated the instructor performance [17].
The increase in digitalization caused us to have plenty of data in every field. Having too much data is getting worth if we know how to use it.
The methods to be used in data mining may differ depending on the field of study and the nature of the data we have. In this study, three well-known classification algorithms (decision tree, random forest, and naive Bayes) were employed on the educational datasets to predict the final grades of students.
Naive Bayes classifiers are a family of algorithms. These classifiers are based on Bayes’ Theorem, which finds the possibility of a new event based on previously occurring events. Each classification is independent of one another but has a common principle.
A decision tree uses a tree like graph. Decision trees are like flowchart but not noncyclic. The tree consists of nodes and branches. Nodes and branches are arranged in a row. Root node is on the top of a tree and represents the entire dataset. Entropy is calculated when determining nodes in a tree. It models decisions with efficacy, results, and resource costs. In this study, decision tree technique is preferred because it is easy to understand and interpret.
Random forest is an ensemble learning algorithm. It is a supervised classification method. It consists of randomly generated many decision trees. The established forest is formed by the decision trees community trained by the bagging method, which is one of the ensemble methods. Random forest creates multiple decision trees and combines them to achieve a more accuracy rates and stable prediction.
Figure 1 illustrates the workflow of data mining model for classification. In the first step, feature selection algorithms are applied on the educational data. Next, classification algorithms are used to build a good model which can accurately map inputs to desired outputs. The model evaluation phase provides feedback to the feature selection and learning phases for adjustment to improve classification performance. Once a model is built, then, in the second phase, it is used to predict label of new student data.
Flowchart of the data mining model.
In this study, the feature subset selection and classification operations were conducted by using WEKA open-source data mining software [18]. In each experiment, 10-fold cross-validation was performed to evaluate the classification models. The classification accuracy of the algorithm for the test dataset was measured as given in Eq. 1:
where
In this study, two publically available datasets [19] were used to predict student performances. Both datasets were collected from secondary education of two Portuguese schools. Dataset attributes are about student grades and social, demographic, and school-related features. All data were obtained from school reports and questionnaires. The first dataset has information regarding the performances of students in Mathematics lesson, and the other one has student data taken from Portuguese language lesson. Both datasets have 33 attributes as shown in Table 1.
Feature | Description | Type | Values |
---|---|---|---|
Sex | The gender of the student | Binary | Female or male |
Age | The age of the student | Numeric | From 15 to 22 |
School | The school of the student | Binary | GP (Gabriel Pereira) or MS (Mousinho da Silveira) |
Address | Home address type of student | Binary | Urban or rural |
Pstatus | Cohabitation status of student’s parent | Binary | Living together or apart |
Medu | Education of student’s mother | Numeric | From 0 to 4 |
Mjob | Job of student’s mother | Nominal | Teacher, health, services, at home, others |
Fedu | Education of student’s father | Numeric | From 0 to 4 |
Fjob | Job of student’s father | Nominal | Teacher, health, services, at home, others |
Guardian | Guardian of student | Nominal | Mother, father, or otherd |
Famsize | Size of family | Binary | “LE3” (less or equal to 3) or “GT3” (greater than 3) |
Famrel | Quality of family relationships | Numeric | From 1 very bad to 5 excellent |
Reason | Reason of choosing this school | Nominal | Close to home, school reputation, course preference, or others |
Travel time | Travel time of home to school | Numeric | 1–<15 min., 2–15 to 30 min., 3–30 min. to 1 hour, or 4–>1 hour |
Study time | Study time of a week | Numeric | –< 2 hours, 2–2 to 5 hours, 3–5 to 10 hours or 4− > 10 hours |
Failures | Number of past class failures | Numeric | n if 1 < =n < 3, else 4 |
Schoolsup | Extra educational school support | Binary | Yes or no |
Famsup | Family educational support | Binary | Yes or no |
Activities | Extracurricular activities | Binary | Yes or no |
Paid class | Extra paid classes | Binary | Yes or no |
Internet | Internet access at home | Binary | Yes or no |
Nursery | Attended nursery school | Binary | Yes or no |
Higher | Wants to take higher education | Binary | Yes or no |
Romantic | With a romantic relationship | Binary | Yes or no |
Free time | Free time after school | Numeric | From 1 (very low) to 5 (very high) |
Go out | Going out with friends | Numeric | From 1 (very low) to 5 (very high) |
Walc | Alcohol consumption of weekend | Numeric | From 1 (very low) to 5 (very high) |
Dalc | Alcohol consumption of workday | Numeric | From 1 (very low) to 5 (very high) |
Health | Status of current health | Numeric | From 1 (very low) to 5 (very high) |
Absences | Number of school absences | Numeric | From 0 to 93 |
G1 | Grade of first period | Numeric | From 0 to 20 |
G2 | Grade of second period | Numeric | From 0 to 20 |
G3 | Grade of final period | Numeric | From 0 to 20 |
The main characteristics of the dataset.
In the raw dataset, the final grade is in the range of 0–20 as with many European countries, where 0 is the worst grade and 20 is the best score. Since the final grade of the students is in the form of integer, the predicted class should be in the form of categorical values, the data needed to be transformed to categories according to a grading policy. In the study, we used and compared two different grading systems: five-level grading and binary grading systems.
We first categorized the final grade in five groups. These ranges are defined based on the Erasmus system. As shown in Table 2, the range 0–9 refers to grade F, which is the worst grade and corresponds to “fail” label. The others (10–11, 12–13, 14–15, and 16–20) correspond to D (sufficient), C (satisfactory), B (good), and A (excellent/very good) class labels, respectively.
1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|
Excellent/very good | Good | Satisfactory | Sufficient | Fail |
16–20 | 14–15 | 12–13 | 10–11 | 0–9 |
A | B | C | D | F |
Five-level grading categories.
To compare the results, we also categorized the final grade as “passed” and “fail.” As shown in Table 3, the range of 0–9 corresponds to F, and it means “fail”; the range of 10–20 refers to A, B, C, and D, and it means “pass.”
Pass | Fail |
---|---|
10–20 | 0–9 |
A, B, C, D | F |
Binary fail/pass category.
As a preprocessing operation, the final grade attribute was categorized according to two different grading systems, before classification. As a result, we have created two versions of both datasets. Both math and Portuguese datasets were available in both five-level and binary grading versions. Hence, we have the chance to compare the results of these versions.
In the first experiment, three algorithms [decision tree (J48), random forest, and naive Bayes] were compared on the five-level grading version and binary version of the Portuguese dataset. As shown in Table 4, the best performance for the five-level grading version for this dataset was obtained with an accuracy rate of 73.50% with the random forest algorithm. However, this accuracy rate was increased with binary grading version of this dataset. In the dataset, where the final grade is categorized in binary form (passing or failing), the accuracy rate was increased to 93.07%.
Algorithm | Five-level grading | Binary grading (P/F) |
---|---|---|
Decision tree (J48) | 67.80% | 91.37% |
Random forest | ||
Naive Bayes | 68.26% | 88.44% |
Classification accuracy rates for the Portuguese lesson dataset.
(accuracy values, bold – best model).
The performances of three classification algorithms on mathematics datasets (five-level and binary label dataset versions) are shown in Table 5. The best results for five-level grading version were obtained with the decision tree (J48) algorithm with an accuracy rate of 73.42%. The best accuracy rate 91.39% for binary dataset version was obtained with the random forest ensemble method.
Mathematics | Five-level grading | Binary grading (P/F) |
---|---|---|
Decision tree (J48) | 89.11% | |
Random forest | 71.14% | |
Naive Bayes | 70.38% | 86.33% |
Classification accuracy rates for the mathematics lesson dataset.
(accuracy values, bold – best model).
As a second experiment, we made all comparisons after dataset preprocessing, in other terms, after feature subset selection. Hence, the most appropriate attributes were selected by using wrapper subset method to increase the accuracy rates.
One of the important steps to create a good model is attribute selection. This operation can be done in two ways: first, select relevant attributes, and second, remove redundant or irrelevant attributes. Attribute selection is made to create a simple model, to create a model that is easier to interpret, and to find out which features are more important for the results. Attribute selection can be done using filters and wrapper methods. In this study, we use the wrapper method, because it generally produces better results. This method has a recursive structure. The process starts with selecting a subset and induces the algorithm on that subset. Then evaluation is made according to the success of the model. There are two options in this assessment. The first option returns to the top to select a new subset, the second option uses the currently selected subset.
In Table 6, the accuracy rates were compared before and after the attribute selection process for the Portuguese dataset for five-level grade version. Thanks to the wrapper subset method, the accuracy rate of the J48 algorithm has increased from 67.80 to 74.88% with the selected attributes. This accuracy rate increased from 68.26 to 72.57% for naive Bayes algorithm. For the random forest method where we get the best accuracy results, the accuracy rate has increased from 73.50 to 77.20%.
Feature selection | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|
Before | 67.80% | 68.26% | 73.50% |
After | 74.88% | 72.57% | |
Selected features | Search backward: age, famsize, Mjob, schoolsup, paid, internet, go out, health, G1, G2 | Search backward: travel time, romantic, free time, health, G1, G2 | School, Travel time, G2 |
Before and after feature selection with five-level grading system.
The obtained classification accuracy rates for the Portuguese lesson dataset with five-level grading system.
(accuracy values, bold – best model).
In Table 7, the accuracy rates were compared before and after the attribute selection process for the mathematics dataset for five-level grading version. In this dataset, attribute selection significantly increased our accuracy. Here, unlike Portuguese language dataset, the best jump was obtained with J48 algorithm and search forward technique in wrapper method. In this way, the accuracy rate increased from 73.42 to 79.49%. A close result was obtained with the search backward technique and accuracy increased from 73.42 to 78.23%. Through this way, naive Bayes and random forest methods also increased significantly. This method increased the accuracy rate of naive Bayes method from 70.38 to 74.18%. Random forest result is increased from 71.14 to 78.99%. These results show that attribute selection with this wrapper subset method also works in this dataset.
Feature selection | Wrapper subset (J48) | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|---|
Before | 73.42% | 73.42% | 70.38% | 71.14% |
After | 78.23% | 74.18% | 78.99% | |
Selectedfeatures | Search backward: age, pstatus, Medu, Fedu, Fjob, failures, schoolsup, paid, activities, famrel, Dalc, Walc, G2 | Search forward: sex, Mjob, Walc, G2 | Famsize, Medu, Fjob, activities, higher, romantic, free time, G2 | Famsize, Fedu, schoolsup, paid, activities, higher, romantic, Walc, absences, G1, G2 |
Before and after feature selection with binary grading system.
The obtained classification accuracy rates for the mathematics lesson dataset with five-level grading system.
(accuracy values, bold – best model).
In Table 8, the results of the wrapper attribute selection method before and after the application to the Portuguese binary version are compared. There was no significant increase in accuracy. The best results were obtained with random forest. The best jump was experienced by the naive Bayes method but did not reach the random forest value. Naive Bayes result has risen from 88.44 to 89.68%. Random forest maintained the high accuracy achieved before the attribute selection and increased from 93.07 to 93.22%.
Feature selection | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|
Before | 91.37% | 88.44% | 93.07% |
After | 91.99% | 89.68% | |
Selected Features | School, age, address, Medu, Fjob, travel time, study time, schoolsup, nursery, higher, famrel, free time, G1, G2 | Sex, age, Pstatus, Fedu, Mjob, Fjob, reason, failures, famsup, paid, higher, Internet, romantic, go out, health, absences, G1, G2 | School, sex, age, address, famsize, Pstatus, Medu, Mjob, Fjob, reason, guardian, travel time, study time, failures, schoolsup, famsup, paid, activities, higher, Internet, romantic, famrel, free time, go out, Dalc, Walc, health, absences, G1, G2 |
Before and after feature selection.
The obtained classification accuracy rates for the Portuguese lesson dataset with binary grading system.
(accuracy values, bold – best model).
After successful results in five-level grade versions, we tried the same attribute selection method in binary label version dataset. Table 9 shows the accuracy values before and after the wrapper attribute selection for the mathematical binary dataset version. Because the accuracy of the binary version is already high, the jump is less than the five-level grades. But again, there is a nice increase in accuracy. The accuracy rate of the J48 algorithm was increased from 89.11 to 90.89%, while the naive Bayes result was increased from 86.33 to 89.11%. As with the mathematics five-level grade dataset, the best results were obtained with random forest in binary label dataset. Accuracy rate increased from 91.39 to 93.67%.
Feature selection | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|
Before | 89.11% | 86.33% | 91.39% |
After | 90.89% | 89.11% | |
Selected features | School, age, address, Medu, Fedu, guardian, failures, schoolsup, famsup, Internet, romantic, famrel, free time, G1, G2 | Sex, age, Pstatus, Fedu, Mjob, Fjob, reason, failures, famsup, paid, higher, Internet, romantic, go out, health, absences, G1, G2 | Address, famsize, Fedu, Mjob, Fjob, reason, guardian, study time, schoolsup, higher, famrel, go out, absences, G2 |
Before and after feature selection.
The obtained classification accuracy rates for the mathematics lesson dataset with binary grading system.
(accuracy values, bold – best model).
As a result, it can be possible to say that accuracy rates have changed positively in all trials using wrapper subset attribute selection method.
This paper proposes the application of data mining techniques to predict the final grades of students based on their historical data. Three well-known classification techniques (decision tree, random forest, and naive Bayes) were compared in terms of accuracy rates. Wrapper feature subset selection method was used to improve the classification performance. Preprocessing operations on the dataset, categorizing the final grade field into five and two groups, increased the percentage of accurate estimates in the classification. The wrapper attribute selection method in all algorithms has led to a noticeable increase in accuracy rate. Overall, better accuracy rates were achieved with the binary class method for both mathematics and Portuguese dataset.
In the future, different feature selection methods can be used. In addition, different classification algorithms can also be utilized on the datasets.
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