Predicting Student Academic Performance at Degree Level: A Case Study

Asif, Rameez; Merceron, Agathe; Pathan, Mahmood K.

doi:10.5815/ijisa.2015.01.05

Cited by 67 publications

(75 citation statements)

References 22 publications

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“…In e-learning, clustering can be used to group students according to their collaboration competence level in a collaborative learning environment [7], predict their academic performance [21] and group students in order to give them differentiated guidance according to their learning skills and other characteristics [22]. In this study, we demonstrate how Skmeans and Expectation Maximization (EM) clustering algorithms can be used to cluster students based on their collaboration competence level.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

Using Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environment

Maina¹,

Oboko²,

Waiganjo³

2017

IJISA

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Abstract-The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners" collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners" collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner"s collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners" collaboration competence level.

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Section: Clustering Algorithmsmentioning

confidence: 99%

Using Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environment

Maina¹,

Oboko²,

Waiganjo³

2017

IJISA

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“…There are many classifiers to implement data mining methods in order to perform in a better way can also apply in the education domain [4]. There are some classifiers which outperforms better than others.…”

Section: Data Mining Approaches and Techniquesmentioning

confidence: 99%

“…Raheela Asif et al [4] presented a case study based on predicting student academic performance at the end of university degree of the degree program at early stage which can help universities to emphasis on skilled students and to initially detect with low educational accomplishment and find effective ways to support them. The four academic cohorts" data comprising 347 undergraduate students have been extracted by using different classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…For the successful adoption of educational data mining, it is very necessary to have wide-ranging of pedagogical data so that the various data mining techniques can be applied on that to enhance learning process after analyzing students" academic data to monitor pedagogical progress with improvements, to predict and improve retention of student at an early stage, and to analyze the chances of failures or mistakes by the students in a learning system [4]. Predicting and enhancing students learning has become more challenging in order to improve student"s grades and thus benefit educational institutes in adaptation of different learning strategies [5].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Engineering Students Pedagogical Progress

Mahboob¹,

Syed²,

Ur³

et al. 2018

ijacsa

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Abstract-Students' pedagogical progress plays a pivotal role in any educational institute in order to pursue imperative education. Educational institutes, Universities, Colleges implement various performance measures in order to keep analyzing and tracking progress of students to cultivate benefits of education in a better way. There are several data mining techniques to apply on education in order to build constructive educational strategies and solutions. This study aims to analyze and track engineering under graduate student's records to judge quality education, student motivation towards learning, and student pedagogical progress to maintain education at high quality level and predicting engineering student's forthcoming progress. Different engineering discipline students' (of three different cohorts) data have been analyzed for tracing current as well as future pedagogical progress based on their sessional (preexamination) marks. In this research, the classification techniques by k-nearest neighbor, Naïve Bayes and decision trees are applied to evaluate different engineering technologies student's performance and also there are different methodologies that can be used for data classification.

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“…In the paper [8], presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program. Naïve Bayes has given an accuracy of 83.65% on Dataset II.…”

Section: Related Workmentioning

confidence: 99%