The improvement of analytics in massive open online courses by applying data mining techniques

Maté, Alejandro; Gregorio, Elisa de; Cámara, José; Trujillo, Juan; Luján-Mora, Sergio

doi:10.1111/exsy.12119

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Human–computer interaction is achieved through the learner's browsing of platform texts, pictures, animations, videos, and taking quizes, developing models, etc. ( 42 ); human–human interaction is mainly achieved through interaction between learners and teachers and between learners ( 43 ). Through the course discussion module, teachers can answer queries from students according to the teaching content, and various forms of discussion can be carried out between students and groups to realize the benign interaction between teachers and students and between students ( 44 , 45 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model

Zhu

Zhao

Wang

et al. 2022

Front. Public Health

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With the spread of COVID-19 worldwide, online education is rapidly catching on, even in some underdeveloped countries and regions. Based on Bandura's ternary learning theory and literature review, this paper takes online learning of college students as the research object and conducts an empirical survey on 6,000 college students in East China. Based on literature review and factor analysis and structural equation model, this paper discusses the relationship among learning cognition, learning behavior, and learning environment in online learning of college students. The learning behavior includes interactive communication, self-discipline mechanism, classroom learning, and study after class. The learning environment includes teaching ability, knowledge system, platform support, process control, and result evaluation; learning cognition includes learning motivation, information perception, and adaptability. It is found that the learning environment has a significant positive impact on learning behavior, and learning cognition has a significant positive impact on learning behavior. It is uncertain whether the learning environment significantly impacts learning cognition. At the learning environment level, the teaching ability (0.59) has the most significant impact on the learning environment, followed by result evaluation (0.42), platform support (0.40), process control (0.33), and knowledge system (0.13). In terms of the influence on learning behavior, classroom learning has the most significant impact (0.79), followed by self-discipline mechanism (0.65), study after class (0.54), and interactive communication (0.44). In terms of learning cognition, information perception (0.62) has the most significant influence, followed by learning motivation (0.50) and adaptability (0.41). This paper suggests strengthening the construction of platforms and digital resources to create a more competitive learning environment. Improve process management and personalized online services, constantly stimulate students' enthusiasm for independent online learning, and cultivate students' online independent learning ability to promote the sustainable and healthy development of online education.

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Section: Literature Reviewmentioning

confidence: 99%

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model

Zhu

Zhao

Wang

et al. 2022

Front. Public Health

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“…The advantages of Weka are therefore the analysis and application of the entire process of machine learning, from data pre-processing, creating a model by choosing one of many realized machine learning algorithms, and making predictions based on input data. Many researchers have used this tool because of its intuitive user interface and easy way to get results from a trained model [33][34][35][36][37][38].…”

Section: Analysis Of Existing Solutionsmentioning

confidence: 99%

Visual Simulator for Mastering Fundamental Concepts of Machine Learning

Milakovic¹,

Draskovic²,

Nikolić³

2022

Applied Sciences

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Machine learning (ML) has become an increasingly popular choice of scientific research for many students due to its application in various fields. However, students often have difficulty starting with machine learning concepts due to too much focus on programming. Therefore, they are deprived of a more profound knowledge of machine learning concepts. The purpose of this research study was the analysis of introductory courses in machine learning at some of the best-ranked universities in the world and existing software tools used in those courses and designed to assist in learning machine learning concepts. Most university courses are based on the Python programming language and tools realized in this language. Other tools with less focus on programming are quite difficult to master. The research further led to the proposal of a new practical tool that users can use to learn without needing to know any programming language or programming skills. The simulator includes three methods: linear regression, decision trees, and k-nearest neighbors. In the research, several case studies are presented with applications of all realized ML methods based on real problems.

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“…Research methodology-focused. Papers in this category discussed improving data analysis and visualization (e.g., Pardos, Whyte, & Kao, 2016), or new data mining techniques (e.g., Maté, De Gregorio, Cámara, Trujillo, & Luján-Mora, 2016).…”

Section: Question Five: What Was the Main Focus Of These Case Studies?mentioning

confidence: 99%

Case Study as a Research Method for Analyzing MOOCs

Montes-Rodríguez

Martínez-Rodríguez

Fernández

2019

IRRODL

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Educational research is one of the many fields of knowledge that frequently use case studies as a research method, particularly when applying an interpretive approach. Based on literature reviews and a systematic analysis of current scientific literature, this paper examines the prevalence and characteristics of the case study as a methodology for research on MOOCs. Ninety-two documents were selected from the search results returned by two of the most prestigious scientific databases: Web of Science (WOS) and SCOPUS. Findings showed that (a) even when searching solely for case studies, quantitative research paradigms were more prevalent than interpretive approaches; (b) geographical distribution of these studies was partially biased; (c) case studies were less prevalent in these databases than other empirical investigations on MOOCs; (d) the data collection and data analysis methods most frequently used in the case studies were more aligned with a quantitative approach; and (e) there is still very little instructor-focused research using this methodology. In the light of these findings and their discussion, future directions for research using case study methodology are proposed, given the potential of this method to illustrate certain issues for which other approaches have proved inadequate or insufficient.

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The improvement of analytics in massive open online courses by applying data mining techniques

Cited by 7 publications

References 19 publications

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model

Visual Simulator for Mastering Fundamental Concepts of Machine Learning

Case Study as a Research Method for Analyzing MOOCs

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