Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Bystrova, Tatiana; Larionova, Viola; Синицын, Е. В.; Толмачев, А. А.

doi:10.17323/1814-9545-2018-4-139-166

Cited by 26 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intelligent content recommendation systems use artificial intelligence methodologies to provide tailored educational resources that align with the preferences and objectives of individual learners (Zhang et al, 2020). The use of AI-powered data-driven learning analytics allows educators to extract valuable insights from learner data (Bystrova et al, 2018), which can be utilised to inform instructional strategies and facilitate continuous improvement. (Troussas et al, 2019) The advancements that have been made in technology have had a substantial impact on education (See et al, 2021), resulting in improved learner involvement (McDonald et al, 2014), acquisition of knowledge(Al-Emran & Teo, 2019), and academic achievements in the era of digitalization (Adi et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Learning: An Exploratory Study on The Impact of Technology-Enhanced Learning Using Digital Learning Platforms and AI Tools on The Study Habits of University Students Through Focus Group Discussions

Khanduri,

Teotia

2023

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

This study investigates the revolutionizing learning: an exploratory study on the impact of technology-enhanced learning using digital learning platforms and AI tools on the study habits of university students through focus group discussions. Nineteen participants were divided into groups of 4-5 members each, and the discussions were thematically analysed. Eight key themes emerged from the analysis, including changing study habits, advantages, and drawbacks of technology in education, challenges in technology-enhanced learning, benefits and limitations of conventional teaching methods, concerns surrounding technology use, and the impact of AI on education. While the findings largely align with existing literature, certain negative effects of technology use were identified, such as a decline in creativity, reduced cognitive functioning, decreased retention and memorization, and lower productivity. These findings highlight the importance of adopting a balanced approach to integrating technology in education, considering both the benefits and potential drawbacks.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Learning: An Exploratory Study on The Impact of Technology-Enhanced Learning Using Digital Learning Platforms and AI Tools on The Study Habits of University Students Through Focus Group Discussions

Khanduri,

Teotia

2023

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

show abstract

“…MOOC research methods (Cluster 5): This cluster indicated that learning analytics was the central keyword, followed by machine learning, content analysis, social network analysis and educational data mining. The publications on implementation of learning analytics in MOOCs (see Bystrova et al, 2018;Yulianto et al, 2018) showed that emerging research methods are used to predict learners' probability of drop out and success, and their performance.…”

Section: Social Network Analysis Of the Abstractmentioning

confidence: 99%

Surfing on Three Waves of MOOCs: An Examination and Snapshot of Research in Massive Open Online Courses

Bozkurt

2021

Open Praxis

View full text Add to dashboard Cite

The purpose of this research is to examine the research that has been done on MOOCs by applying data mining and analytic approaches and to depict the current state of MOOC research. The text mining revealed four broad themes: (I) MOOCs as a mainstreaming learning model in HE, (II) motivation and engagement issues in MOOCs, (III) assessment issues in MOOCs, and (IV) MOOCs for social learning. The social network analysis indicated that MOOCs are a significant aspect of online education and that learning analytics are considered a solution to current MOOC handicaps. Both pivotal contribution analysis and timeline analysis demonstrated that MOOC research has a heavy focus on motivation and engagement, high drop out and low retention rates, and instructional quality. Overall, the research concludes that the first wave of MOOC research largely concentrated on the qualitative characteristics of the phenomenon, while the second and third waves of MOOC research concentrated on the quantitative characteristics. 297 Bozkurt Open Praxis

show abstract

“…Massive Open Online Courses (MOOCs) are a relatively recent aspect of ITS research, and we identify our earliest instance of this within our corpus in 2013 [38]. We identify 38% of research in this topic entailing learning analytics [39] [40] [41], which involves the wealth of data provided by MOOC platforms. This data may be applied to dropout prediction and forecasting of MOOC platforms [42] [43], and we identify 11% of documents involving dropout prediction.…”

Section: Topic 12 -Moocsmentioning

confidence: 99%

Wide-Scale Automatic Analysis of 20 Years of ITS Research

Hodgson

Cristea

Shi

et al. 2021

Intelligent Tutoring Systems

View full text Add to dashboard Cite

The analysis of literature within a research domain can provide significant value during preliminary research. While literature reviews may provide an in-depth understanding of current studies within an area, they are limited by the number of studies which they take into account. Importantly, whilst publications in hot areas abound, it is not feasible for an individual or team to analyse a large volume of publications within a reasonable amount of time. Additionally, major publications which have gained a large number of citations are more likely to be included in a review, with recent or fringe publications receiving less inclusion. We provide thus an automatic methodology for the large-scale analysis of literature within the Intelligent Tutoring Systems (ITS) domain, with the aim of identifying trends and areas of research from a corpus of publications which is significantly larger than is typically presented in conventional literature reviews. We illustrate this by a novel analysis of 20 years of ITS research. The resulting analysis indicates a significant shift of the status quo of research in recent years with the advent of novel neural network architectures and the introduction of MOOCs.

show abstract

Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance

Cited by 26 publications

References 6 publications

Revolutionizing Learning: An Exploratory Study on The Impact of Technology-Enhanced Learning Using Digital Learning Platforms and AI Tools on The Study Habits of University Students Through Focus Group Discussions

Revolutionizing Learning: An Exploratory Study on The Impact of Technology-Enhanced Learning Using Digital Learning Platforms and AI Tools on The Study Habits of University Students Through Focus Group Discussions

Surfing on Three Waves of MOOCs: An Examination and Snapshot of Research in Massive Open Online Courses

Wide-Scale Automatic Analysis of 20 Years of ITS Research

Contact Info

Product

Resources

About