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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 courses…

6184

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

Open Access
Article
Publication date: 11 April 2023

Lolowa Almekhaini, Ahmad R. Alsuwaidi, Khaula Khalfan Alkaabi, Sania Al Hamad and Hassib Narchi

Computer-Assisted Learning in Pediatrics Program (CLIPP) and National Board of Medical Examiners Pediatric Subject Examination (NBMEPSE) are used to assess studentsperformance

Abstract

Purpose

Computer-Assisted Learning in Pediatrics Program (CLIPP) and National Board of Medical Examiners Pediatric Subject Examination (NBMEPSE) are used to assess studentsperformance during pediatric clerkship. International Foundations of Medicine (IFOM) assessment is organized by NBME and taken before graduation. This study explores the ability of CLIPP assessment to predict studentsperformance in their NBMEPSE and IFOM examinations.

Design/methodology/approach

This cross-sectional study assessed correlation of students’ CLIPP, NBMEPSE and IFOM scores. Students’ perceptions regarding NBMEPSE and CLIPP were collected in a self-administered survey.

Findings

Out of the 381 students enrolled, scores of CLIPP, NBME and IFOM examinations did not show any significant difference between genders. Correlation between CLIPP and NBMEPSE scores was positive in both junior (r = 0.72) and senior (r = 0.46) clerkships, with a statistically significant relationship between them in a univariate model. Similarly, there was a statistically significant relationship between CLIPP and IFOM scores. In an adjusted multiple linear regression model that included gender, CLIPP scores were significantly associated with NBME and IFOM scores. Male gender was a significant predictor in this model. Results of survey reflected students’ satisfaction with both NBMEPSE and CLIPP examinations.

Originality/value

Although students did not perceive a positive relationship between their performances in CLIPP and NBMEPSE examinations, this study demonstrates predictive value of formative CLIPP examination scores for their future performance in both summative NBMEPSE and IFOM. Therefore, students with poor performance in CLIPP are likely to benefit from feedback and remediation in preparation for summative assessments.

Details

Arab Gulf Journal of Scientific Research, vol. 42 no. 2
Type: Research Article
ISSN: 1985-9899

Keywords

Open Access
Article
Publication date: 12 October 2021

Kiran Fahd, Shah Jahan Miah and Khandakar Ahmed

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of…

3668

Abstract

Purpose

Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.

Design/methodology/approach

This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.

Findings

Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.

Originality/value

The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

Open Access
Article
Publication date: 25 October 2019

Ning Yan and Oliver Tat-Sheung Au

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction…

7613

Abstract

Purpose

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

Design/methodology/approach

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Findings

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

Originality/value

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

Details

Asian Association of Open Universities Journal, vol. 14 no. 2
Type: Research Article
ISSN: 2414-6994

Keywords

Open Access
Article
Publication date: 17 November 2023

Zamira Hyseni Duraku, Linda Hoxha, Jon Konjufca, Artë Blakaj, Blerinë Bytyqi, Erona Mjekiqi and Shkurtë Bajgora

This pilot study aims to examine the prevalence of test anxiety and its interplay with attitudes, confidence, efficacy, academic performance and socio-demographic factors within…

Abstract

Purpose

This pilot study aims to examine the prevalence of test anxiety and its interplay with attitudes, confidence, efficacy, academic performance and socio-demographic factors within the domain of science, technology, engineering and mathematics (STEM) courses.

Design/methodology/approach

The authors employed a quantitative, cross-sectional design with 549 sixth-grade students from public lower secondary schools in Prishtina, Kosovo, using the Student Attitudes Toward STEM Survey (S-STEM) for middle/high schools and the test anxiety questionnaire.

Findings

Over 70% of Kosovo's sixth-grade students reported moderate to severe test anxiety. The age of students was found to be inversely related to academic performance in STEM. The father's employment was associated with favorable STEM attitudes, confidence, efficacy and academic performance. Having a personal study environment was connected with favorable STEM attitudes, confidence and efficacy in STEM, whereas access to technology was associated with positive academic performance. Test anxiety, academic performance and personal study space predicted students' attitudes, confidence and efficacy in STEM and 21st-century learning.

Practical implications

Educational institutions should prioritize student well-being. By addressing test anxiety, these institutions can create supportive learning environments that improve attitudes, confidence and efficacy in STEM fields. These efforts are crucial for STEM career development and student success in the 21st-century workforce.

Originality/value

The current study findings contribute to a deeper understanding of the factors influencing STEM student engagement and performance, highlighting the importance of addressing test anxiety for positive learning outcomes while emphasizing the need to consider socio-economic and contextual factors in education.

Details

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

Keywords

Open Access
Article
Publication date: 28 January 2019

Bothaina A. Al-Sheeb, A.M. Hamouda and Galal M. Abdella

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any…

5464

Abstract

Purpose

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of studentsperformance by introducing two methodologies based on variable selection and dimensionality reduction.

Design/methodology/approach

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

Findings

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

Research limitations/implications

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

Practical implications

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

Originality/value

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

Details

Journal of Applied Research in Higher Education, vol. 11 no. 2
Type: Research Article
ISSN: 2050-7003

Keywords

Open Access
Article
Publication date: 18 May 2020

Denise M. Wilson, Lauren Summers and Joanna Wright

This study investigated how behavioral and emotional forms of engagement are associated with faculty support and student-faculty interactions among engineering students.

2768

Abstract

Purpose

This study investigated how behavioral and emotional forms of engagement are associated with faculty support and student-faculty interactions among engineering students.

Design/methodology/approach

Quantitative research methods were used to analyze survey data from 781 undergraduates in seven large undergraduate engineering courses. Linear hierarchical regression models were used to evaluate the relationships between demographics (gender, race/ethnicity, family education, US status and transfer status) and student engagement and between faculty behaviors and engagement.

Findings

Faculty support was consistently, significantly and positively linked to all forms of student engagement, while student-faculty interactions were significantly and positively linked to effort and positive emotional engagement and negatively linked to attention and (an absence of) negative emotional engagement. Gender, race/ethnicity, international student status and transfer status significantly predicted at least one form of engagement.

Research limitations/implications

Although this was a single institution study and cross-sectional, the findings suggest that faculty support and student-faculty interactions, while important for engagement, have different effects on different types of students. Faculty and teacher professional development efforts should raise awareness of these differences in order to enhance diversity and inclusion in engineering courses and curricula at all levels.

Originality/value

The analysis of behavioral and emotional forms of engagement represents more of a motivational lens on engagement in contrast to the traditional focus on time-on-task or time spent in fruitful educational practices, as is the norm with much of the engagement literature in higher education.

Details

Journal of Research in Innovative Teaching & Learning, vol. 13 no. 1
Type: Research Article
ISSN: 2397-7604

Keywords

Open Access
Article
Publication date: 1 June 2007

Kate O’Neill and Peter Theuri

Literature is replete with studies indicating the need to develop students’ language skills; however, little research has emphasized the importance of language proficiency in…

Abstract

Literature is replete with studies indicating the need to develop students’ language skills; however, little research has emphasized the importance of language proficiency in enhancing learning or performance in specific content-area courses. This study investigates whether a student’s English language proficiency can be associated with her performance in specific cognitive skills (knowledge, comprehension, application, and analysis) in an introductory accounting course. Data is summarized from studentsperformance on their first financial accounting examination as well as from students’ academic history records as maintained by the university. A correlation analysis of the cognitive skills score with student language proficiency is used to identify initial relationships; and multiple regression analysis is subsequently used to identify interrelations between combined multiple dependent variables and the language proficiency variables. While the results show no association between TOEFL and overall performance, the mean of the English composition courses do show a significant association with knowledge and comprehension cognitive skills scores on the first financial accounting course. No associations are attached to the application and analysis cognitive skills. The results are meaningful to faculty in balancing language proficiency with quality instruction in content-area courses.

Details

Learning and Teaching in Higher Education: Gulf Perspectives, vol. 4 no. 1
Type: Research Article
ISSN: 2077-5504

Open Access
Article
Publication date: 2 May 2017

Billy Tak Ming Wong

The purpose of this paper is to present a systematic review of the mounting research work on learning analytics.

21806

Abstract

Purpose

The purpose of this paper is to present a systematic review of the mounting research work on learning analytics.

Design/methodology/approach

This study collects and summarizes information on the use of learning analytics. It identifies how learning analytics has been used in the higher education sector, and the expected benefits for higher education institutions. Empirical research and case studies on learning analytics were collected, and the details of the studies were categorized, including their objectives, approaches, and major outcomes.

Findings

The results show the benefits of learning analytics, which help institutions to utilize available data effectively in decision making. Learning analytics can facilitate evaluation of the effectiveness of pedagogies and instructional designs for improvement, and help to monitor closely students’ learning and persistence, predict studentsperformance, detect undesirable learning behaviours and emotional states, and identify students at risk, for taking prompt follow-up action and providing proper assistance to students. It can also provide students with insightful data about their learning characteristics and patterns, which can make their learning experiences more personal and engaging, and promote their reflection and improvement.

Originality/value

Despite being increasingly adopted in higher education, the existing literature on learning analytics has focussed mainly on conventional face-to-face institutions, and has yet to adequately address the context of open and distance education. The findings of this study enable educational organizations and academics, especially those in open and distance institutions, to keep abreast of this emerging field and have a foundation for further exploration of this area.

Details

Asian Association of Open Universities Journal, vol. 12 no. 1
Type: Research Article
ISSN: 1858-3431

Keywords

Open Access
Article
Publication date: 2 May 2017

Choo Jun Tan, Ting Yee Lim, Chin Wei Bong and Teik Kooi Liew

The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with…

1670

Abstract

Purpose

The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement.

Design/methodology/approach

A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.

Findings

The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy.

Originality/value

A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

Details

Asian Association of Open Universities Journal, vol. 12 no. 1
Type: Research Article
ISSN: 1858-3431

Keywords

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