Study on learning effect prediction models based on principal component analysis in MOOCs

Zhang, Wei; Qin, Shiming; Yi, Baolin; Tian, Peng

doi:10.1007/s10586-018-2594-0

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The principle of effective teaching is universally applicable, and all teachers and educational administrators should consider what types of teaching are effective and how these can be achieved (Sofyan et al, 2021). Overall, the effective teaching method usually means that teachers follow the objective laws of teaching activities, use professional teaching behaviors, rationally design the teaching process, distribute their limited time, energy, and material resources evenly among their students, realize their teaching objectives and students' personality cultivation and all-round development, and achieve as many effects from teaching as possible (Li and Liu, 2015;Zhang et al, 2019). These aspects are reflected in the effectiveness of the whole teaching process, namely, effective teaching organization, effective teaching objectives, effective interaction, effective curriculum content, effective learning results, etc.…”

Section: Introductionmentioning

confidence: 99%

Influencing factors for effective teaching evaluation of massively open online courses in the COVID-19 epidemics: An exploratory study based on grounded theory

Liu

Wang

2022

Front. Psychol.

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Many factors affect the teaching of massively open online courses (MOOCs). In this study, to explore the factors that influence the effective teaching of MOOCs, a large number of relevant studies are analyzed. Based on grounded theory, semi-structured interviews were conducted with 30 students and teachers who used MOOCs for online teaching. The interview data were subjected to four research processes –open coding, axial coding, selective coding, and saturation testing– to explore the factors influencing MOOCs’ effective teaching and the interactions between them. The results demonstrate that: (1) Effective teachers, effective tuition, effective communication, active online learning, social support guarantees, and online course design have important positive effects on effective teaching, while only certain online learning behaviors will seriously affect the teaching effectiveness of MOOC, resulting in negative effects. (2) Effective communication is essential for effective teaching in MOOCs; effective teachers are the leading factor, thus teachers should take the initiative to study and understand the students to understand their various learning needs and difficulties. (3) Reasonable and effective classroom teaching design is key to improving MOOCs’ teaching efficiency. (4) E-learning is respected, cared for, and valued by society, including cognition, emotion, and learning platform support from family, school, teachers, and classmates, and has an important impact on students’ motivation and the effects of online learning. The results of this study further clarify factors influencing effective teaching of MOOCs, thus helping to enrich and supplement the theory of effective teaching and evaluation and providing theoretical guidance for teachers to effectively implement MOOC teaching.

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Section: Introductionmentioning

confidence: 99%

Influencing factors for effective teaching evaluation of massively open online courses in the COVID-19 epidemics: An exploratory study based on grounded theory

Liu

Wang

2022

Front. Psychol.

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Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

Alshmrany

2021

Cluster Comput

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Study on learning effect prediction models based on principal component analysis in MOOCs

Cited by 2 publications

References 9 publications

Influencing factors for effective teaching evaluation of massively open online courses in the COVID-19 epidemics: An exploratory study based on grounded theory

Influencing factors for effective teaching evaluation of massively open online courses in the COVID-19 epidemics: An exploratory study based on grounded theory

Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

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