Applying big data analysis technique to students' learning behavior and learning resource recommendation in a MOOCs course

Su, Yu-Sheng; Ding, Ting-Jou; Lue, Jiann-Hwa; Lai, Chin‐Feng; Su, Chiu-Nan

doi:10.1109/icasi.2017.7988114

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…MOOC was recorded to be used from 2008 (Corbeil, Khan, & Corbeil, 2018). Through MOOCs and social media platforms, users can share, track, and search for information on their specific interests (Su, Ding, Lue, Lai, & Su, 2017). Taiwanese higher education institutions focused on MOOCs to increase students` interests in education (Hsieh, 2019).…”

Section: Moocmentioning

confidence: 99%

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A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Adnan

Ariffin

Hanafi

et al. 2021

AJUE

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Recently, the promotion of Science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective students’ interest in STEM and also Technical and Vocational Education and Training (TVET) subjects. However, very little research has been done for the higher education institutions in Malaysia in leveraging social media and social media analytics effectively to increase the students’ interests and awareness of STEM and TVET disciplines. Therefore, this paper aims to propose a framework to increase prospective students’ interest in STEM and TVET using social media and big data analytics. The objectives of this study are to explore various social media applications in education and study these applications towards increasing students’ interests and propose a suitable framework for Malaysian higher education institutions. The framework is proposed by following the theory synthesis methodology. Four main components of the framework have been proposed, namely social media, role model or mentoring, massive open online courses and big data analytics. Each component is significant and requires a considerable amount of time to develop. The suggested framework is anticipated to benefit higher education institutions with a significant gain of the number of students, revenues and positive reputations. Keywords: Social media, Social media analytics, STEM, E-learning, Education

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Section: Moocmentioning

confidence: 99%

“…The MOOC can be integrated with big data analytics to identify students` interactions, unknown correlations, and hidden patterns. The outcome of the study is the recommendation of the students` learning behavior (Su et al, 2017).…”

Section: Moocmentioning

confidence: 99%

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Adnan

Ariffin

Hanafi

et al. 2021

AJUE

View full text Add to dashboard Cite

show abstract

“…In 2017, Su et al proposed a big data analysis technique on learning portfolio to explore how the interaction, unknown correlations, and hidden patterns among learners on MOOCs and social media platforms start and recognize the using big data analytics transforming on students' learning behavior [20]. Martina et al analyzed data from users of an online platform that provides learning materials for medical students in preparation for their final exams and found correlations between the number of cards studied and the number of test questions that had been answered with the percentage of correct answers in the learners' medical exams through hierarchical regression analysis [21].…”

Section: Introductionmentioning

confidence: 99%

Do Gender or Major Influence the Performance in Programming Learning? Teaching Mode Decision Based on Exercise Series Analysis

Gao

Zhang

Ruipeng

et al. 2022

Computational Intelligence and Neuroscience

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Both traditional teaching and online teaching advocate individualized education. One of the difficulties on exploring possible improvements of instructional design is the challenging process of data collection. Existing research mainly focuses on the exam score of students but pays little attention to students' daily practice. As an effective method to handle time-series dataset, the generalized estimating equations (GEE) have not been used in this research field. Considering above issues, we first propose an experimental paradigm of programming performance analysis based on the performance record of students’ daily practice-exam and finish collecting a complete time-series dataset in one semester, including students’ individual attributes, learning behavior, and learning performance. Then, we propose an approach that analyzes practice-exam time-series dataset based on GEE to study the influence of individual attributes and learning behavior on learning performance. It is the first time to apply the GEE method for ordinal multinomial responses in this research field, by which we conclude several results that gender or major does have a certain difference on the programming learning. The longer the answer time and the less the cost time, the better the students' performance. Regardless of gender, students tend to cram for the exam and perform a little worse in the daily exercise. Finally, targeting at two important individual attributes, we give corresponding teaching mode decisions that university should teach students programming by major and teacher should give different teaching methods to students of different genders at different time points.

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A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

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Applying big data analysis technique to students' learning behavior and learning resource recommendation in a MOOCs course

Cited by 8 publications

References 6 publications

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education

Do Gender or Major Influence the Performance in Programming Learning? Teaching Mode Decision Based on Exercise Series Analysis

A Systematic Mapping Review on MOOC Recommender Systems

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