Profile-Based Cluster Evolution Analysis: Identification of Migration Patterns for Understanding Student Learning Behavior

Priyambada, Satrio Adi; Er, Mahendrawathi; Yahya, Bernardo Nugroho; Usagawa, Tsuyoshi

doi:10.1109/access.2021.3095958

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…Models such as cellular automata (CA), cyber engagement (CE), predictive game theory model (PGTM) for programming students, and profile-based cluster evolution analysis (PBCEA) take incremental inputs. Additives to the equation include depression (35), programming skills (36), and migratory patterns (37). CA, CE, PGTM, and PBCEA can all achieve 79 percent accuracy on a variety of datasets.…”

Section: IImentioning

confidence: 99%

PMSIEMDL: Design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning Fusions

Sarika Khandelwal

2024

jes

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Identification of student inclination towards different educational fields requires integration of deep pattern learning models with temporal data analysis techniques. These techniques are highly context sensitive, and cannot be scaled for analysis of students that have an interest in multiple domains. Moreover, existing deep learning models are highly complex, and showcase moderate performance when used on real-time datasets. To overcome these limitations, this text proposes design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning fusions. The proposed model initially collects data samples from a large number of students, and segregates them into different classes. These include social data, personal habits data, education data, family related data, performance data and future inspiration data classes. These datasets were combined with a customized psychological questionnaire which was curated by experts in the field of student counselling & psychology. Based on student responses, their entity specific classes were generated, that were separately trained via different Convolutional Neural Network (CNN) Models, which assists in identification of student-performance at individual-class levels. These performances are compared with existing inclination datasets via a fusion of Long-Short-Term Memory (LSTM) & Gated Recurrent Neural Network (GRNN), which assists in identification of correlation between subject-level inclinations & their entity classes. This provides with a probabilistic map of different subjects towards which the student might be inclined, and assists them to select their study streams. The generated map was validated for multiple students, and recommendations were made based on higher probability values, which assisted in identification of student inclination levels. The model was evaluated under large datasets and its performance was compared with various state-of-the-art methods under different scenarios. Based on this comparison, it was observed that proposed model was capable of achieving 8.5% better recommendation accuracy, 4.9% higher prediction precision, 6.5% better recommendation recall & 2.9% better Area Under the Curve (AUC) levels, which makes it highly useful for a wide variety of student inclination use cases

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Section: IImentioning

confidence: 99%

PMSIEMDL: Design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning Fusions

Sarika Khandelwal

2024

jes

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“…Another research work on learning behavior, in particular, examines student migration patterns regarding the conformity of courses taken with curriculum guidelines [24]. The clustering technique is based on a limited set of educational and academic records such as grades, courses, IP, and timestamps.…”

Section: B Clustering Approach In Edmmentioning

confidence: 99%

Educational Data Mining Clustering Approach: Case Study of Undergraduate Student Thesis Topic

Andre,

Suciati,

Fabroyir

et al. 2023

IEEE Access

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This study aims to investigate the potential of educational data mining (EDM) to address the issue of delayed completion in undergraduate student thesis courses. The problem of delayed completion of these courses is a common issue that impacts both students and higher education institutions. The study employed clustering analysis to create clusters of thesis topics. The research model was constructed by using expert labeling to assign each thesis title to a computer science ontology standard. Cross-referencing was employed to associate supporting courses with each thesis title, resulting in a labeled dataset with three supporting courses for each thesis title. This study analyzed five different clustering algorithms, including K-Means, DBScan, BIRCH, Gaussian Mixture, and Mean Shift, to identify the best approach for analyzing undergraduate thesis data. The results demonstrated that K-Means clustering was the most efficient method, generating five distinct clusters with unique characteristics. Furthermore, this research investigated the correlation between educational data, specifically GPA and the average grades of courses that support a thesis title and the duration of thesis completion. Our investigation revealed a moderate correlation between GPA, thesis-supporting course average grades, and the time to complete the thesis, with higher academic performance associated with shorter completion times. These moderate results indicate the need for further studies to explore additional factors beyond GPA and the average grades of thesis-supporting courses that contribute to thesis completion delays. This study contributes to understanding and evaluating the educational outcomes within study programs as defined in the curriculum, particularly concerning the design and implementation of thesis topics. Additionally, the clustering results serve as a foundation for future research and offer valuable insights into the potential of using EDM techniques to assist in selecting appropriate thesis topics, thereby reducing the risk of delayed completion.

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“…Several works have been conducted on analyzing the students' performance in the coherent vertical curriculum by using cluster analysis both on aggregated and segmented data [5], [10]. There is also research implementing the cluster evolution analysis to analyze the migration of the students' learning behavior [6]. However, it is difficult to get the insight effectively from this kind of results because it was used to monitor the performance of the student in current semester rather than predict the future performances.…”

Section: Educational Data Miningmentioning

confidence: 99%

“…The profile-based approach has been done by clustering the students per unit of time to understand students' learning behavior in comparison to the curriculum [5]. Also, the changes of the behavior from semester to semester have been analyzed using the migration of the students' learning behavior of the student at a certain period of time by clustering method based on students' profiles [6]. However, no clear results can be obtained by only analyzing the cluster of the students.…”

Section: Introductionmentioning

confidence: 99%

Progressive Prediction of Students’ Future Performance on Coherent Vertical Curriculum

Priyambada

Wafda

Usagawa

et al. 2022

IIAI Letters on Institutional Research

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Predicting students' future performance is important for academic stakeholders as the students' success is the objective of the higher educational institutes. The prediction based on past performance and alignment with the curriculum is crucial to support decision-making action effectively for the university with a coherent vertical curriculum. The result of the prediction can be used to intervene and ensure that the student can graduate on time, also preventing the student from dropping out. In this paper, we proposed a methodology for predicting progressively the students' future assessment including feature engineering. Using the ensemble learning techniques, we adapt the existing Ensemble-based Progressive Prediction so it can be applied on students' data that used the Coherent Vertical Curriculum. In this paper, the behavioral data is used instead of domain knowledge-based data. The results show that the algorithm's accuracy has been improved on a real-world student dataset.

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Profile-Based Cluster Evolution Analysis: Identification of Migration Patterns for Understanding Student Learning Behavior

Cited by 14 publications

References 16 publications

PMSIEMDL: Design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning Fusions

PMSIEMDL: Design of a Pattern analysis Model for identification of Student Inclination towards different Educational-fields via Multimodal Deep Learning Fusions

Educational Data Mining Clustering Approach: Case Study of Undergraduate Student Thesis Topic

Progressive Prediction of Students’ Future Performance on Coherent Vertical Curriculum

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