Ontology-based group assessment analytics framework for performances prediction in project-based collaborative learning

Hadyaoui, Asma; Cheniti-Belcadhi, Lilia

doi:10.1186/s40561-023-00262-w

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Hadyaoui and Cheniti-belcadhi [33] introduced an ontology-based approach for the analytics of group assessment which examined the impact of intra-group interactions inside project-based collaborative learning (PBCL). It also forecasted the performance of learners depending on their interactions.…”

Section: Literature Surveymentioning

confidence: 99%

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

2024

IJATEE

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Learning analytics (LA) is the measuring, gathering, analyzing, and reporting of data on learners and their environments. This data is used to analyze and improve e-learning. LA has traditionally been used for a variety of purposes, including the prediction of student academic progress and more specifically, the identification of students who are in danger of failing a course or quitting their studies. However, the majority of the existing schemes have the issue of accurately predicting the students' performance in online courses. In this paper, correlation-based self-attention networklong short-term memory (SAN-LSTM) is proposed to predict students' outcomes, along with the effectiveness of the teaching experience, as well as the assessment methods. Initially, the data is collected from three datasets namely, WorldUC, Liru, and Junyi to evaluate the performance of the proposed approach. The min-max normalization is employed to improve the performance of the approach. The correlation-based feature selection (CFS) is employed to select appropriate features from the pre-processed data. Finally, the correlation-based SAN-LSTM is established to forecast the effectiveness of fine-grained learning. Three real-world datasets gathered from various e-learning empirically validated that the proposed model improves prediction outcomes and provides useful data for formative evaluation. The existing methods such as adaptive sparse self-attention network (AS-SAN), Bangor engagement metric (BEM), and deep belief network learning style (DBNLS) are used for comparison to justify the effectiveness of the correlation-based SAN-LSTM method. The proposed correlation-based SAN-LSTM achieves better results of 98% of accuracy and 93% of precision. The proposed method achieves 98% accuracy which is higher when compared to those of AS-SAN, BEM, and DBNLS.

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Section: Literature Surveymentioning

confidence: 99%

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

2024

IJATEE

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Uncovering the factors underlying collaboration outcomes in interprofessional education: A preliminary investigation involving healthcare students

Shen,

Dizon,

Chong

et al. 2024

Nurse Education Today

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Personalized and adaptive e-learning systems for semantic Web: a systematic review and roadmap

Iqbal,

Sarwar,

Safyan

et al. 2024

IJWIS

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Purpose The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems. Design/methodology/approach Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners. Findings The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes. Research limitations/implications The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process. Social implications E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status. Originality/value A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

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Ontology-based group assessment analytics framework for performances prediction in project-based collaborative learning

Cited by 4 publications

References 32 publications

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

Uncovering the factors underlying collaboration outcomes in interprofessional education: A preliminary investigation involving healthcare students

Personalized and adaptive e-learning systems for semantic Web: a systematic review and roadmap

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