Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors

Chen, Wei-Yu; Brinton, Christopher G.; Cao, Da; Mason-Singh, Amanda; Lu, Charlton; Chiang, Mung

doi:10.1109/tlt.2018.2793193

Cited by 49 publications

(34 citation statements)

References 23 publications

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“…Excellent correlation coefficients are obtained, and 85% of predictions are within one point of the actual grade, outperforming traditional regression algorithms. [8] focus on Short courses with a single result assigned by the teacher at the end. Due to a lack of performance data and relatively limited enrollments, learner activity recorded as they engage with course content and with one another in Social Learning Networks (SLN) is critical for prediction.…”

Section: Ghorbani R and Ghousi R (2020) [5]mentioning

confidence: 99%

Study Of Students’ Performance Prediction Models Using Machine Learning

Viswanathan¹

2021

TURCOMAT

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Educational Data Mining (EDM) is a novel concept associated with developing methods for exploring the specific types of data produced by educational settings and using those approaches to effectively understand students and the environments in which they learn. Prediction attempts to shape trends that will allow it to predict results or learning outcomes based on available data. Predicting student success has become an appealing challenge for researchers. They develop an understandable and efficient model using supervised and unsupervised EDM techniques. This assists decision-makers in improving student performance. The task of deciding the best model leads to the emergence of various techniques from both EDM techniques. The numerous research models used to solve the problem of student success prediction using educational data mining are discussed in this paper. The primary purpose of this paper is to explain the methodology for implementing the proposed solution for student performance prediction, as well as to present the findings of a study aimed at evaluating the performance of various data mining classification algorithms on the given dataset in order to assess their potential usefulness for achieving the goal and objectives.

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Section: Ghorbani R and Ghousi R (2020) [5]mentioning

confidence: 99%

Study Of Students’ Performance Prediction Models Using Machine Learning

Viswanathan¹

2021

TURCOMAT

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“…The authors in [128] addressed the graph analysis problem in multi-source relational learning for educational data. When the numbers of nodes in multiple graphs are large, the labeled training instances are extremely sparse.…”

Section: Graph Analyticsmentioning

confidence: 99%

“…Our approach is based on graph-based analytics like the techniques proposed by [127], [128]. The graph-based analytics approach was selected due to its effectiveness for cross-university transfer learning where courses may come from different providers and across institutions.…”

Section: B Technological Challengesmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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“…The rationale of using student logs or behavioral data collected while interacting with learning management systems is that they serve as indicators of student engagement and efforts in the course, which have been shown to be positively related to (Chen and Jang, 2010;Davies and Graff, 2005;de Barba et al, 2016;Kizilcec et al, 2013;Morris et al, 2005;Tempelaar et al, 2015). Given the large amount of data accumulated through the course of an academic term, statistical procedures such as correlation analysis have been used in the literature as an initial step (Chen et al, 2018) to identify relevant student activities that could help predict course performance.…”

Section: Data Sources and Student Variablesmentioning

confidence: 99%

Predictive analytic models of student success in higher education

Cui

Shiri

et al. 2019

ILS

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Purpose Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education. Design/methodology/approach Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions Findings In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education. Originality/value This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.

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Early Detection Prediction of Learning Outcomes in Online Short-Courses via Learning Behaviors

Cited by 49 publications

References 23 publications

Study Of Students’ Performance Prediction Models Using Machine Learning

Study Of Students’ Performance Prediction Models Using Machine Learning

Big Educational Data & Analytics: Survey, Architecture and Challenges

Predictive analytic models of student success in higher education

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