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Article
Publication date: 5 February 2018

Olugbenga Wilson Adejo and Thomas Connolly

The purpose of this paper is to empirically investigate and compare the use of multiple data sources, different classifiers and ensembles of classifiers technique in…

1014

Abstract

Purpose

The purpose of this paper is to empirically investigate and compare the use of multiple data sources, different classifiers and ensembles of classifiers technique in predicting student academic performance. The study will compare the performance and efficiency of ensemble techniques that make use of different combination of data sources with that of base classifiers with single data source.

Design/methodology/approach

Using a quantitative research methodology, data samples of 141 learners enrolled in the University of the West of Scotland were extracted from the institution’s databases and also collected through survey questionnaire. The research focused on three data sources: student record system, learning management system and survey, and also used three state-of-art data mining classifiers, namely, decision tree, artificial neural network and support vector machine for the modeling. In addition, the ensembles of these base classifiers were used in the student performance prediction and the performances of the seven different models developed were compared using six different evaluation metrics.

Findings

The results show that the approach of using multiple data sources along with heterogeneous ensemble techniques is very efficient and accurate in prediction of student performance as well as help in proper identification of student at risk of attrition.

Practical implications

The approach proposed in this study will help the educational administrators and policy makers working within educational sector in the development of new policies and curriculum on higher education that are relevant to student retention. In addition, the general implications of this research to practice is its ability to accurately help in early identification of students at risk of dropping out of HE from the combination of data sources so that necessary support and intervention can be provided.

Originality/value

The research empirically investigated and compared the performance accuracy and efficiency of single classifiers and ensemble of classifiers that make use of single and multiple data sources. The study has developed a novel hybrid model that can be used for predicting student performance that is high in accuracy and efficient in performance. Generally, this research study advances the understanding of the application of ensemble techniques to predicting student performance using learner data and has successfully addressed these fundamental questions: What combination of variables will accurately predict student academic performance? What is the potential of the use of stacking ensemble techniques in accurately predicting student academic performance?

Details

Journal of Applied Research in Higher Education, vol. 10 no. 1
Type: Research Article
ISSN: 2050-7003

Keywords

Article
Publication date: 12 June 2017

Ali Hasan Alsaffar

The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student

Abstract

Purpose

The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course.

Design/methodology/approach

The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines.

Findings

The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable.

Originality/value

This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 14 July 2021

Faisal A. Abdelfattah, Omar S. Obeidat, Yousef A. Salahat, Maha B. BinBakr and Adam A. Al Sultan

This study examined predictors of cumulative grade point average (GPA) from entrance scores and successive performance during students' academic work in university…

Abstract

Purpose

This study examined predictors of cumulative grade point average (GPA) from entrance scores and successive performance during students' academic work in university engineering programs.

Design/methodology/approach

Scores from high school coursework, the General Ability Test and the Achievement Test were examined to determine if these factors and annual successive GPAs were predictors of long-term GPA. The sample consisted of 2,031 students registered in university engineering programs during the 2013–2019 period.

Findings

Correlations were significant between entrance scores and the preparatory year GPA but not with cumulative GPA. Also, correlations were significant between year-1 GPA to year-3 GPA and the graduation GPA. Adjacent year GPA is the better predictor of later GPA. More importantly, GPA at the time of graduation is well predicted by GPAs throughout years of study within engineering programs after controlling for entrance scores. Girls outperform boys in their entrance scores and GPAs. Hence, girls are likely to obtain higher cumulative GPAs.

Research limitations/implications

The implications of the study findings could help university faculty and administrators to understand the role of current entrance scores in predicting academic achievement of engineering students. In addition, the results could serve as a foundation to review weights of entrance scores for future developments and revisions. The findings of the study are limited to admission data for engineering students during the 2013–2019 period. Other disciplines may show a different pattern of relationships among the studied variables.

Practical implications

The study findings have useful practical implications for admitting and monitoring student progress at engineering education programs. Results may help program curriculum development specialists and committees in designing admission criteria.

Social implications

Administrators and faculty members are advised to consider entrance scores when providing counseling and monitoring throughout students' program-year progress. More attention should be devoted to university performance when interest is focused on later or graduation CGPA, with less emphasis on entrance scores.

Originality/value

The existed previous studies explored factors that influence the student performance in engineering programs. This study documents the role of admission criteria and successive GPAs in predicting the student graduation CGPA in engineering programs. Relationships between factors are crucial for engineering program revisions and policymaking.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

Keywords

Article
Publication date: 8 February 2020

Ying Cui, Fu Chen and Ali Shiri

This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances

Abstract

Purpose

This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data?

Design/methodology/approach

Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing.

Findings

The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks.

Originality/value

The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.

Details

Information and Learning Sciences, vol. 121 no. 3/4
Type: Research Article
ISSN: 2398-5348

Keywords

Article
Publication date: 28 August 2009

Mohd Daud Norzaidi and Mohamed Intan Salwani

Using the extended task‐technology fit (TTF) model, this paper aims to examine technology resistance, technology satisfaction and internet usage on students' performance.

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Abstract

Purpose

Using the extended task‐technology fit (TTF) model, this paper aims to examine technology resistance, technology satisfaction and internet usage on students' performance.

Design/methodology/approach

The study was conducted at Universiti Teknologi MARA, Johor, Malaysia and questionnaires were distributed to 354 undergraduate students.

Findings

The structural equation modelling (SEM) results indicate that technology satisfaction and the internet usage significantly explains the variance on students' performance. Task‐technology fit is not a predictor of technology resistance but it does predict the internet usage. The internet usage has greater impact on technology satisfaction than technology satisfaction on the internet usage. Finally, technology resistance is not a predictor of students' performance.

Research limitations/implications

The study focuses only on education in Malaysia and concentrates only on the students' performance and the relationship between technology resistance, technology satisfaction and the internet usage.

Practical implications

The results provide insights on how Malaysian education systems of a similar structure could improve upon their internet adoption.

Originality/value

This study is perhaps one of the first to address internet adoption in education using an extended task‐technology fit model (task‐technology fit, internet usage, technology resistance, technology satisfaction) to investigate their influences on students' performance.

Details

Campus-Wide Information Systems, vol. 26 no. 4
Type: Research Article
ISSN: 1065-0741

Keywords

Article
Publication date: 16 August 2019

Gomathy Ramaswami, Teo Susnjak, Anuradha Mathrani, James Lim and Pablo Garcia

This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student

Abstract

Purpose

This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.

Design/methodology/approach

Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.

Findings

The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.

Research limitations/implications

The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of studentsperformance. Future work will address some of these limitations.

Originality/value

The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

Details

Information and Learning Sciences, vol. 120 no. 7/8
Type: Research Article
ISSN: 2398-5348

Keywords

Article
Publication date: 30 August 2021

Sarah Alturki and Heiner Stuckenschmidt

The purpose of this study is to determine whether students' self-assessment (SSA) could be used as a significant attribute to predict students' future academic achievement.

Abstract

Purpose

The purpose of this study is to determine whether students' self-assessment (SSA) could be used as a significant attribute to predict students' future academic achievement.

Design/methodology/approach

The authors address how well students can assess their abilities and study the relationship between this ability and demographic properties and previous study performance. The authors present the study results by measuring the relationship between the SSA across five different topics and comparing them with the students' performance in these topics using short tests. The test has been voluntarily taken by more than 300 students planning to enroll in the School of Business Informatics and Mathematics master's programs at the University of Mannheim.

Findings

The study results reveal which attributes are mostly associated with the accuracy level of SSA in higher education. The authors conclude that SSA, it can be valuable in predicting master's students' academic achievement when taking specific measures when designing the predictive module.

Research limitations/implications

Due to time constraints, the study was restricted only to students applying to master's programs at the Faculty of Business Informatics and Mathematics at the University of Mannheim. This resulted in collecting a limited data set. Also, the scope of this study was restricted to testing the accuracy of SSA and did not test using it as an attribute for predicting students' academic achievement.

Originality/value

Predicting students' academic performance in higher education is beneficial from different perspectives. The literature reveals that a considerable amount of work is published to analyze and predict academic performance in higher education. However, most of the published work relies on attributes such as demographics, teachers' assessment, and examination scores for performing their prediction while neglecting the use of other forms of evaluation such as SSA or self-evaluation.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

Keywords

Open Access
Article
Publication date: 3 July 2017

Rahila Umer, Teo Susnjak, Anuradha Mathrani and Suriadi Suriadi

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online…

4973

Abstract

Purpose

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Design/methodology/approach

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of studentsperformance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

Findings

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Practical implications

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Social implications

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

Originality/value

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

Details

Journal of Research in Innovative Teaching & Learning, vol. 10 no. 2
Type: Research Article
ISSN: 2397-7604

Keywords

Article
Publication date: 1 July 1997

Maury A. Peiperl and Rose Trevelyan

Reports on a study of MBA students (N = 362) at a major international business school which looked at the predictors of performance in management education. Considers not…

1246

Abstract

Reports on a study of MBA students (N = 362) at a major international business school which looked at the predictors of performance in management education. Considers not only GMAT but also age, gender, language proficiency, marital status and work experience as predictors of performance. Questions the use of individual grades in assessing performance since much work in both business schools and the business community is done in groups. Therefore, an analysis of the performance of students in groups was also carried out. Results support the relationship between GMAT and age, and individual performance, and more importantly show a predictive ability for language proficiency and marital status. Significantly, no predictors of group performance were found. Overall, the performance of groups was better than the performance of individuals. Discusses the implications of these results.

Details

Journal of Management Development, vol. 16 no. 5
Type: Research Article
ISSN: 0262-1711

Keywords

Article
Publication date: 7 January 2019

Kanwal Nasim and Muhammad Zahid Zahid Iqbal

The purpose of this paper is to know that how group resources (internal and external) and the relationship quality among group members relate to group performance.

Abstract

Purpose

The purpose of this paper is to know that how group resources (internal and external) and the relationship quality among group members relate to group performance.

Design/methodology/approach

Given the normative nature of group performance, the study is carried out in a contrived environment. Participants were 204 master of business administration students who were allocated to 51 study groups. Data were collected in three waves and from two different sources, i.e., students and instructors. Data analysis was carried out by employing regression analysis and the bootstrapping procedure, i.e., PROCESS.

Findings

The results of this paper reveal that an individual-level internal resource, i.e. time, positively predicts group performance, while group-level internal resources, i.e., group composition and group members’ experience, negatively predict group performance. Both external resources (external communication and instructor’s support) are found to have a positive effect on group performance. The relationship quality among group members partially relates to group performance. Instructor’s support as an external resource is found to moderate the relationship between only two aspects of relationship quality and group performance.

Practical implications

This study provides guidance to group members as to how they can utilize internal and external group resources and their relationship quality for enhancing their group performance. Managers in varied organizations can also utilize the findings of this study.

Originality/value

This study is unique in that it offers a new insight into internal and external resources and relationship quality, that is, from the perspective of group performance. The group resources included in the study are rarely found in the existing literature.

Details

International Journal of Productivity and Performance Management, vol. 68 no. 3
Type: Research Article
ISSN: 1741-0401

Keywords

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