A two-phase machine learning approach for predicting student outcomes

Iatrellis, Omiros; Savvas, Ilias Κ.; Fitsilis, Panos; Gerogiannis, Vassilis C.

doi:10.1007/s10639-020-10260-x

Cited by 61 publications

(39 citation statements)

References 40 publications

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“…The data related to students' demographic, academic and social behavior was collected through a survey. Iatrellis et al (2020) proposed a machine learning approach wherein K-Means algorithm generates a set of coherent clusters and afterward supervised machine learning algorithms are used to train prediction models for predicting students' performance. Maesya and Hendiyanti (2019) developed model to forecast if the student will graduate on time or late than the standard graduation duration.…”

Section: Literature Reviewmentioning

confidence: 99%

An artificial intelligence approach to monitor student performance and devise preventive measures

Khan

Ahmad

Jabeur

et al. 2021

Smart Learn. Environ.

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A major problem an instructor experiences is the systematic monitoring of students’ academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to collect enormous amount of data concerning their students from various sources, however, the institutes are craving novel procedures to utilize the data to magnify their prestige and improve the education quality. This research evaluates the effectiveness of machine learning algorithms to monitor students’ academic progress and informs the instructor about the students at the risk of ending up with unsatisfactory result in a course. In addition, the prediction model is transformed into a clear shape to make it easy for the instructor to prepare the necessary precautionary procedures. We developed a set of prediction models with distinct machine learning algorithms. Decision tree triumph over other models and thus is further transformed into easily explicable format. The final output of the research turns into a set of supportive measures to carefully monitor students’ performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students.

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Section: Literature Reviewmentioning

confidence: 99%

An artificial intelligence approach to monitor student performance and devise preventive measures

Khan

Ahmad

Jabeur

et al. 2021

Smart Learn. Environ.

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“…The Random Forest method was introduced by Breiman ( 2001) as a machine learning algorithm (Iatrellis et al, 2021;Pliakos et al, 2019;Beaulac & Rosenthal, 2019). It has been applied as integrated statistical learning classification and regression algorithm.…”

Section: Random Forestsmentioning

confidence: 99%

Predicting the percentage of student placement: A comparative study of machine learning algorithms

Çakıt

Dağdeviren

2021

Educ Inf Technol

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In recent years, there has been an increase in the demand for higher education in Turkey, where the demand, as in most other countries, exceeds what is available. The main purpose of this research is to develop machine learning algorithms for predicting the percentage of student placement based on the data related to the university's academic reputation, opportunities of the city where the university is located, facilities and cultural opportunities of the university. When the model accuracy was evaluated on the basis of performance metrics, the Extreme Gradient Boosting (XGBoost) algorithm showed greater predictive accuracy than other machine learning approaches. A sensitivity analysis was performed using the extreme gradient boosting machines algorithm to identify the degree to which the input variables contribute to the determination of the output variable. Five input variables, namely the percentage of student placement at year t-1, the university scientific document score, university Phd programme score, university faculty member/student score, and the percentage of student placement at year t-2 were found to be the most effective parameters. Prediction and sensitivity analysis results obtained in this study can be used in many different ways, such as determining the quotas for universities, allocating resources, and making new regulations.

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“…enrollment date, enrollment test marks, the number of courses students previously enrolled in, type of study program and study mode) [16,17], tertiary academic (e.g. attendance, number of assessment submissions, student engagement ratio, major, time left to complete the degree, course credits, semester work marks, placements and count and date of attempted exams) [4,[14][15][16][17][18] and LMS-based data have all been studied in previous analyses regarding the prediction of student academic performance.…”

Section: Important Attributes For Predicting Student Academic Performancementioning

confidence: 99%

“…Student record such as grade point average (GPA) has been frequently used as a categorical variable, as have a semester or final results of a student [4,[8][9][10][11][12][14][15][16][17][18][19][20] and the graduate or drop out the status of a student [16]. These are considered to be significant indicators of academic potential.…”

Section: Important Attributes For Predicting Student Academic Performancementioning

confidence: 99%

“…It is easy to understand a reasoning tree of "if-then" rules, with such decision tree based techniques used frequently to predict student academic progress and identify at-risk ones [22]. Each of decision tree [9,10,13,14,19], random forest [8,19,21], J4 [4,7,12,20], decision stump [20], OneR [20], NBTree [20], ID3 [20], PART [12,20], Naive Bayes [9,13], neural networks [9,16], support vector machine [9,21], logistic regression [16,19], ZeroR [20], Prism [12,20], multi-layer perceptron [4] and K-nearest neighbor [9] have been used to predict student progress. Few studies have used clustering ML techniques [15] to improve learning outcomes, of which K-mean has proven popular for the analysis of student data [9,11,14].…”

Section: Techniques Used To Predict Student Academic Performancementioning

confidence: 99%

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Predicting student performance in a blended learning environment using learning management system interaction data

Fahd

Miah

Ahmed

2021

ACI

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PurposeStudent 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/approachThis 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.FindingsIdentifying 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/valueThe best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

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A two-phase machine learning approach for predicting student outcomes

Cited by 61 publications

References 40 publications

An artificial intelligence approach to monitor student performance and devise preventive measures

An artificial intelligence approach to monitor student performance and devise preventive measures

Predicting the percentage of student placement: A comparative study of machine learning algorithms

Predicting student performance in a blended learning environment using learning management system interaction data

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