Big Data-Assisted Recommendation of Personalized Learning Resources and Teaching Decision Support

Huiji, Si

doi:10.3991/ijet.v17i04.29585

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…In order to provide direct and reliable decision support for the promotion of personalized learning resources (PLR), the current research urgently need to overcome several problems of PLR recommendation, such as the recommended PLR not meeting requirements, no dynamical analysis of learning behavior, and no good prediction of learning intention. In order to solve these problems, Huiji [22] discusses big dataassisted PLR recommendation and teaching decision support. Take oral and written language education in the process of exploration as an example, which introduces the flow of the algorithm in detail and proves its effectiveness in experiment.…”

Section: Introductionmentioning

confidence: 99%

Teaching Decision Optimization for Sustainable Development of Students

Kang¹,

Gao²,

Liang³

2023

Int. J. Emerg. Technol. Learn.

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With the shift of the global education view to humanism, more and more scholars and experts have gradually deepened their research on sustainable development ability of students in higher vocational colleges. As for sustainable development of students, the core and basic point is to develop their personality and teach them in accordance with their aptitude. As a bridge and link between teaching thinking and practice of teachers, their teaching decisions need to fully take into consideration the sustainable development of students, which is of great significance to future employment, job transfer and promotion of students. Therefore, this paper studies the teaching decision optimization (TDO) for the sustainable development of students. First of all, a related teaching decision model is constructed. Then this paper proposes the particle swarm optimization algorithm (PSO) of teaching decision path based on progress rate, gives the principle of selecting TDO template, designs three different optimization models for ordinary particles, high-quality particles and progress particles and represents the optimization strategy of teaching decision path. Finally, experimental results verify the effectiveness of the optimization model.

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Section: Introductionmentioning

confidence: 99%

Teaching Decision Optimization for Sustainable Development of Students

Kang¹,

Gao²,

Liang³

2023

Int. J. Emerg. Technol. Learn.

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“…However, as the number of online videos on video websites grows rapidly, the problem of information overload becomes more serious. It becomes difficult for students to get the videos which they really need and are interested in from the mass videos [13][14][15][16][17][18][19][20][21]. Online learning platforms that cannot solve this problem well will gradually lose student user group they already have.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Liu¹

2023

Int. J. Emerg. Technol. Learn.

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Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.

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“…Model persamaan struktural menunjukkan bahwa peningkatan penggunaan teknologi digital dalam lingkungan pembelajaran yang dicirikan oleh metode pengajaran terbuka merupakan aspek penting dalam pembelajaran yang dipersonalisasi karena memiliki efek positif pada keterampilan digital yang dilaporkan sendiri dan keyakinan terkait TIK yang dirasakan sendiri dalam pembelajaran (Schmid & Petko, 2019). Evaluasi fitur pembelajaran yang dipersonalisasi dapat memahami fitur perilaku belajar secara cerdas, dan memberikan dukungan keputusan langsung dan dapat diandalkan untuk mempromosikan sumber daya pembelajaran yang dipersonalisasi (Huiji, 2022). Sesuai dengan uraian tersebut maka peneliti tertarik untuk mengetahui tentang bagaimana sebenarnya tentang implementasi pembelajaran dipersonalisasi menggunakan litercy ICT digital.…”

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Implementasi Personalized Learning Menggunakan Litercy Ict Digital : Sebuah Systematic Literatur Review

Winarno

2024

hinef

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Abstract. The different learning needs of students are met by accommodating and personalizing their education. Technology is often used to facilitate the personalization of student learning environments. However, the educational world is currently struggling to implement such an approach. The change in digital literacy practices in the context of education requires a continuous re-examination of the conceptualization of digital literature education. The research uses the method of systematic literature review (SLR) to trace selected literature from 35 Scopus indexed journals on the implementation of personalized learning using digital ICT literacy. The results of the research show that the urgent need of students for personalized learning becomes more evident as educational information progresses. Personalized Recommendation Technology (PR) can help personalized learning and improve student learning efficiency. The development of personalized learning platforms can be done based on CF (collaborative filtering) algorithms, and perform system needs analysis. But it still requires an evaluation of personalized learning features in order to be able to understand the behavioral features of learning intelligently. Abstrak. Kebutuhan pembelajaran yang berbeda dari siswa dipenuhi dengan mengakomodasi dan mempersonalisasi pendidikan mereka. Teknologi sering digunakan untuk memfasilitasi personalisasi lingkungan belajar siswa. Namun, saat ini dunia pendidikan mengalami kesulitan untuk menerapkan pendekatan tersebut. Perubahan praktik literasi digital dalam konteks pendidikan mengharuskan terus meninjau kembali konseptualisasi pendidikan literasi digital. Penelitian ini menggunakan metode systematic literature review (SLR) untuk menelusuri literatur-literatur terpilih dari 35 jurnal terindeks Scopus tentang implementasi pembelajaran dipersonalisasi menggunakan litercy ICT digital. Hasil penelitian menunjukan kebutuhan mendesak pelajar akan pembelajaran yang dipersonalisasi menjadi lebih jelas seiring dengan kemajuan informasi pendidikan. Teknologi rekomendasi yang dipersonalisasi (PR) dapat membantu pembelajaran yang dipersonalisasi dan meningkatkan efisiensi pembelajaran peserta didik. Pengembangan platform pembelajaran yang dipersonalisasi dapat dilakukan berdasarkan algoritma CF (collaborative filtering), dan melakukan analisis kebutuhan sistem. Tetapi tetap perlu adanya evaluasi fitur pembelajaran yang dipersonalisasi agar dapat memahami fitur perilaku belajar secara cerdas.

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Big Data-Assisted Recommendation of Personalized Learning Resources and Teaching Decision Support

Cited by 7 publications

References 19 publications

Teaching Decision Optimization for Sustainable Development of Students

Teaching Decision Optimization for Sustainable Development of Students

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Implementasi Personalized Learning Menggunakan Litercy Ict Digital : Sebuah Systematic Literatur Review

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