Educational Data Mining Approach Research Articles

Early Bird or Night Owl: Insights into Dutch Students’ Study Patterns using the Medical Faculty’s E-learning Registrations

Phenomenon : Educational activities for students are typically arranged without consideration of their preferences or peak performance hours. Students might prefer to study at different times based on their chronotype, aiming to optimize their performance. While face-to-face activities during the academic schedule do not offer flexibility and cannot reflect students’ natural learning rhythm, asynchronous e-learning facilitates studying at one’s preferred time. Given their ubiquitous accessibility, students can use e-learning resources according to their individual needs and preferences. E-learning usage data hence serves as a valuable proxy for certain study behaviors, presenting research opportunities to explore students’ study patterns. This retrospective study aims to investigate when and for how long undergraduate students used medical e-learning modules. Approach : We performed a cross-sectional analysis of e-learning usage at one medical faculty in the Netherlands. We used data from 562 undergraduate multimedia e-learning modules for pre-clinical students, covering various medical topics over a span of two academic years (2018/19 and 2019/20). We employed educational data mining approaches to process the data and subsequently identified patterns in access times and durations. Findings : We obtained data from 70,805 e-learning sessions with 116,569 module visits and 1,495,342 page views. On average, students used e-learning for 16.8 min daily and stopped using a module after 10.2 min, but access patterns varied widely. E-learning was used seven days a week with an hourly access pattern during business hours on weekdays. Across all other times, there was a smooth increase or decrease in e-learning usage. During the week, more students started e-learning sessions in the morning (34.5% vs. 19.1%) while fewer students started in the afternoon (42.6% vs. 50.8%) and the evening (19.4% vs. 27.0%). We identified ‘early bird’ and ‘night owl’ user groups that show distinct study patterns. Insights : This retrospective educational data mining study reveals new insights into the study patterns of a complete student cohort during and outside lecture hours. These findings underline the value of 24/7 accessible study material. In addition, our findings may serve as a guide for researchers and educationalists seeking to develop more individualized educational programs.

Exploring school factors of academic achievement in Macau: The application of educational data mining in Progress in International Reading Literacy Study (PIRLS) 2016 assessment

International large-scale assessments, provide structured and static data, and due to their extensive databases, they can be considered as a valuable resource for Big Data in Education. In this paper, we propose an educational data mining approach to detect and analyze factors linked to academic performance in Macau schools using data from the Progress in International Reading Literacy Study (PIRLS) 2016. We conducted a secondary data analysis based on a set of socioeconomic, process, and outcome variables from PIRLS and other sources, and built decision trees to obtain a predictive model of school performance. By doing so, we were able to identify the school and student-level variables that are most significant in predicting student performance in Macau. These findings will be useful for informing educational policy decisions and shedding light on the causes of poor performance in Macau schools. Overall, our study highlights the potential of educational data mining approaches in analyzing large-scale assessment data and generating insights for educational research and practice.

Early detection of student degree-level academic performance using educational data mining.

Higher educational institutes generate massive amounts of student data. This data needs to be explored in depth to better understand various facets of student learning behavior. The educational data mining approach has given provisions to extract useful and non-trivial knowledge from large collections of student data. Using the educational data mining method of classification, this research analyzes data of 291 university students in an attempt to predict student performance at the end of a 4-year degree program. A student segmentation framework has also been proposed to identify students at various levels of academic performance. Coupled with the prediction model, the proposed segmentation framework provides a useful mechanism for devising pedagogical policies to increase the quality of education by mitigating academic failure and encouraging higher performance. The experimental results indicate the effectiveness of the proposed framework and the applicability of classifying students into multiple performance levels using a small subset of courses being taught in the initial two years of the 4-year degree program.

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques-Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors-are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs.

Implementation of a Machine Learning-Based MOOC Recommender System Using Learner Motivation Prediction

The phenomenon of high dropout rates has been the concern of MOOC providers and educators since the emergence of this disruptive technology in online learning. This led to the focus on learner motivation studies from different aspects: demotivation signs detection, learning path personalization, course recommendation, etc. Our paper aims to predict learner motivation for MOOCs to select the right MOOC for the right learner. So, we predict the motivation in an educational data mining approach by extracting and preprocessing learners' navigation traces on a MOOC platform and building a machine learning model that predicts accurately a given learner motivation for a MOOC. The comparison of the performance of four supervised learning algorithms resulted in the selection of the random forest classifier as a modeling technique for motivation prediction. Afterward, the Machine Learning-based recommendation function was tested for learners of the MOOC platform dataset to recommend the Top-10 MOOCs suitable for the target learner. Finally, further research on learner characteristics considered in recommender systems could enlarge the recommendation scope of MOOCs and maintain learner motivation.

The use of video clickstream data to predict university students' test performance: A comprehensive educational data mining approach.

Video clickstream behaviors such as pause, forward, and backward offer great potential for educational data mining and learning analytics since students exhibit a significant amount of these behaviors in online courses. The purpose of this study is to investigate the predictive relationship between video clickstream behaviors and students' test performance with two consecutive experiments. The first experiment was performed as an exploratory study with 22 university students using a single test performance measure and basic statistical techniques. The second experiment was performed as a conclusive study with 16 students using repeated measures and comprehensive data mining techniques. The findings show that a positive correlation exists between the total number of clicks and students' test performance. Those students who performed a high number of clicks, slow backward speed or doing backwards or pauses achieved better test performance than those who performed a lower number of clicks, or who used fast-backward or fast-forward. In addition, students' test performance could be predicted using video clickstream data with a good level of accuracy (Root Mean Squared Error Percentage (%RMSE) ranged between 15 and 20). Furthermore, the mean of backward speed, number of pauses, and number/percentage of backwards were found to be the most important indicators in predicting students' test performance. These findings may help educators or researchers identify students who are at risk of failure. Finally, the study provides design suggestions based on the findings for the preparation of video-based lectures.

Internet use behaviour model for predicting students' performance

AbstractInternet technology is vital in education as it is an enabler tool used by students for information exchange, communication and creation of knowledge. However, issues related to Internet use behaviour (IUB) and effects on students' performance are still being debated due to inconsistent results. Thus, the main aim of this paper is to propose a specific IUB classification model to discover comprehensive prediction models on students' performance by using educational data mining (EDM). Data related to the students' CGPA and their internet usage activities were collected from 469 undergraduate students. Primarily, in this study several techniques were ensembled to predict students' performance based on IUB. An EDM approach comprising of clustering, classification, correlation, and regression was used. The base classifiers including decision tree (j48) ensembled with EM clustering technique were exploited to develop a high accuracy of prediction results. At first, the usage of clustering technique was to cluster the dataset into smaller variable and applied classification technique to solve the complexity of the variables. Correlation come across to measure the degree of IUB and students' performance and later regression was applied to produce predictive results. Based on 11 IUB categories, the results indicate that online gaming has a negative significant effect on the students' performance. Additionally, the prediction model revealed a high correlation, which generates good predictions of 92%. The prediction model would enable higher learning institutions to detect as early as possible those students who are at risk. This will then help them in taking timely and proactive measures to improve their performance.

Multiple educational data mining approaches to discover patterns in university admissions for program prediction

<span>This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.</span>

Exploring students digital activities and performances through their activities logged in learning management system using educational data mining approach

PurposeThis paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically.Design/methodology/approachThe LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background.FindingsResults show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS.Originality/valueThis empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

Improving Learners’ Assessment and Evaluation in Crisis Management Serious Games: An Emotion-based Educational Data Mining Approach

For several years, there has been growing interest in the development and use of serious games to improve individuals' quality of life and behavior. In particular, Crisis Management Serious Games (CMSG) have shown their potential for teaching people both technical and soft skills related to managing crises in a safe environment while reducing training costs. To improve their effectiveness, several evaluation approaches of CMSGs have been proposed. However, despite its interest, the learner's emotional state is often neglected. As a result, learners may end up with a deep frustration or boredom. Therefore, we propose an Educational Data Mining (EDM) approach to evaluate learners' affective states in collaborative CMSGs. This approach is applied to assess learners' engagement during a game-based evacuation scenario aiming to train and to raise awareness among students of a university on evacuating the present persons during a fire emergency situation. The experimental results confirm the major predictions of learners' affective dynamics and uncover novel findings namely the presence of transitions from boredom to frustration and from frustration to confusion. Our study shows that combining gaming and emotion aspects under an EDM approach to evaluate CMSGs is interesting since it gives reliable results in a less invasive way.

Educational data mining approaches, challenges and goals: A review

There are various methods in educational data mining to explore data in an educational context as it is an emerging research area. To study educational questions for the analysis of educational data, it uses computational approaches. Since last few years there is an increment in the acceptance rate of learning management systems in education. To study the behavior and performance of the students, various data mining techniques like clustering, prediction and relationship mining can be used with educational data. The motive behind this study is to review unlike techniques and approaches of data mining in an educational context for the structuring a new environment so that academic predictions can be made.

Introductory anatomy and physiology in an undergraduate nursing curriculum.

Using an educational data mining approach, first-year academic achievement of undergraduate nursing students, which included two compulsory courses in introductory human anatomy and physiology, was compared with achievement in a final semester course that transitioned students into the workplace. We hypothesized that students could be grouped according to their first-year academic achievement using a two-step cluster analysis method and that grades achieved in the human anatomy and physiology courses would be strong predictors of overall achievement. One cohort that graduated in 2014 (n = 105) and one that graduated in 2015 (n = 94) were analyzed separately, and for both cohorts, two groups were identified, these being "high achievers" (HIGH) and "low achievers" (LOW). Consistently, the anatomy and physiology courses were the strongest predictors of group assignment, such that a good grade in these was much more likely to put a student into a high-achieving group. Students in the HIGH groups also scored higher in the Transition to Nursing course when compared with students in the LOW groups. The higher predictor importance of the anatomy and physiology courses suggested that if a first-year grade-point average was calculated for students, an increased weighting should be attributed to these courses. Identifying high-achieving students based on first-year academic scores may be a useful method to predict future academic performance.

A Review on Feature Selection Methods for Improving the Performance of Classification in Educational Data Mining

Educational data mining (EDM) evaluates and predicts students' performance that assists to discover important factors affecting students' academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying feature selection algorithms. Feature selection algorithms help in selecting robots and meaningful features for predicting students' performance with high accuracy. This paper presents different EDM approaches for forecasting students' performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in educational data mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.

An efficient educational data mining approach to support e-learning

The e-learning is a recent development that has emerged in the educational system due to the growth of the information technology. The common challenges involved in The e-learning platform include ...

A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining

This paper presents the results of applying an educational data mining approach to model academic attrition (loss of academic status) at the Universidad Nacional de Colombia. Two data mining models were defined to analyze the academic and nonacademic data; the models use two classification techniques, naïve Bayes and a decision tree classifier, in order to acquire a better understanding of the attrition during the first enrollments and to assess the quality of the data for the classification task, which can be understood as the prediction of the loss of academic status due to low academic performance. The models aim to predict the attrition in the student's first four enrollments. First, considering any of these periods, and then, at a specific enrollment. Historical academic records and data from the admission process were used to train the models, which were evaluated using cross-validation and previously unseen records from a full academic period. Experimental results show that the prediction of the loss of academic status is improved when the academic data are added.