Design of adaptive learning system based on big data

Liu, Xiaoxia; Du, Yuejin; Sun, Feiqiang; Zhai, Lidong

doi:10.1145/3078564.3078571

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…DCBVN model is applied in [97] for recommending personalized online courses to employee training and development . Furthermore, some research studies implemented hybrid with DM and statistical techniques [24,105,148]. For example, Morsy and Karypis [105] applied SVD, Course2vec and cumulative Knowledge-based Regression Model to predict sequencing of courses.…”

Section: Hybrid Approachmentioning

confidence: 99%

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The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

Data

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In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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Section: Hybrid Approachmentioning

confidence: 99%

“…Liu et al [148] hybrid with ARM and ML. Only the design is proposed not applied to specific scenarios for evaluation.…”

mentioning

confidence: 99%

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

Data

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Adaptive Learning Model and Implementation Based on Big Data

Liang¹,

Hainan²

2019

2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)

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Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review

Ruiz Nepomuceno,

López Domínguez,

Domínguez Isidro

et al. 2024

Applied Sciences

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Adaptive Mobile Learning Systems (AMLSs) are technological tools that incorporate adaptive learning to generate an individual teaching–learning process for each student. Despite the proliferation of AMLS, their development is often hindered by a lack of standardization and the poor quality of existing software architectures in terms of requirements correctness and coverage. This paper presents a systematic literature review (SLR) of software architectures oriented to developing AMLS reported in the state of the art. For this SLR, we searched the ACM Digital Library, IEEE Xplore, Science Direct, Springer, and SCOPUS. Studies published in English that describe and evaluate a software architecture to develop AMLS were included. Twenty-two primary works were relevant to the present study. Based on the obtained results, we have identified key aspects that must be considered in a software architecture proposed for developing an AMLS in terms of requirements, quality attributes, stakeholders, services provided to users, views and architectural styles, components, and implementation technologies. The main finding of this work is the lack of a reference software architecture that considers all the requirements and quality attributes needed for developing AMLS. This gap hinders the effective development and standardization of quality AMLSs, suggesting a critical need for a comprehensive reference software architecture.

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Design of adaptive learning system based on big data

Cited by 3 publications

References 3 publications

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Adaptive Learning Model and Implementation Based on Big Data

Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review

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