A proposal to a decision support system with learning analytics

Acevedo, Yuri Vanessa Nieto; Herrera‐Viedma, Enrique; Gaona-García, Paulo Alonso; Crespo, Rubén González

doi:10.1109/educon.2018.8363223

Cited by 14 publications

(15 citation statements)

References 13 publications

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“…Learning analytics is a "multi-disciplinary research field that now forms its own domain" (Scheffel et al, 2014, p.117). Ferguson (2012) and others (i.e., Fidalgo-Blanco et al, 2015;Nieto Acevedo and Montenegro Maran, 2015;Scanlon, McAndrew and O'Shea, 2015;Scheffel et al, 2014) cite the generally accepted definition developed at the First International Conference on Learning Analytics and Knowledge in 2011 adopted by the Society for Learning Analytics Research. This definition is quoted as follows:…”

Section: Learning Analytics State-of-the-artmentioning

confidence: 98%

“…1;Moodle, 2015). Other third-party learning analytics tools are available at cost and the free applications remain untested in Moodle (Hatala, Gasevic and Joksimovic, 2014;Nieto Acevedo and Montenegro Maran, 2015;Reyes, 2015;Ruiparez-Valiente et al, 2015;Yahya, Messoussi and Touahni, 2015). At the time of writing, there were no studies examining learning engagement module in Moodle. Several researchers recently studied how learning analytics in Moodle may be used to visualise student activity (Dimopoulos et al, 2013;Dyckhoff et al, 2012).…”

Section: Introductionmentioning

confidence: 98%

“…Learning analytics has been applied to study and visualise the relationship between student activity and performance in online-based university-level courses during the last decade Ferguson, 2012;Gunn, 2014;Nieto Acevedo and Montenegro Maran, 2015;Retalis et al, 2006;Scanlon, McAndrew and O'Shea, 2015). The authors of 11 relevant studies not only published some benefits in peer-reviewed scholarly journals but they also cited many problems when trying to assess student learning through combinations of learning analytics, learning management system (LMS) activity data logs, and graded performance results (Agudo-Peregrina et al, 2014;Fidalgo-Blanco et al, 2015;Gomez-Aguilar et al, 2015;Iglesias-Pradas, Ruiz-de-Azcarate and Agudo-Peregrina, 2015;Nieto Acevedo and Montenegro Maran, 2015;Reyes, 2015;Ruiparez-Valiente et al, 2015;Scheffel et al, 2014;Xing et al, 2015;Yahya, Messoussi and Touahni, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Can online student performance be forecasted by learning analytics?

Strang

2016

IJTEL

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The paper focuses on utilising learning analytics to assess student performance in an online business. The value of this paper is in the literature review and the process used for analysis since it provides a way of how research in Moodle learning analytics or other similar systems could be leveraged to track student progress and for retention. The engagement analytics module is a relatively new component of Moodle but it has not yet been investigated with respect to student performance or in online business courses. The purpose of this study was to examine the predictive relationships between student academic performance and Moodle engagement analytics indicators along with student online activity data from the system logs. Unfortunately none of the hypothesised learning analytics factors were positively related to, nor could they predict, student academic performance. However, several interesting deductions from the learning analytics data gave rise to ideas for further research. Sense making of puzzling statistics suggested a mediating pattern of students' poor self-regulation skills because more focus was put on the assignment requirements but less on interacting with the lesson materials needed to complete the assignment and thereby resulting in lower grades.

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Section: Learning Analytics State-of-the-artmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Can online student performance be forecasted by learning analytics?

Strang

2016

IJTEL

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“…The technique allows great quantities of information to be used in managing and controlling students' behaviour in the academic context. The improvements to be made in the optimisation of the learning process are based on the definition of patterns for decision-making [6,7]. With LA tools, it is possible to investigate what occurs within the black box of a virtual classroom and to identify the processes that are developed by students, using activity records.…”

Section: Introductionmentioning

confidence: 99%

Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring

et al. 2019

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Learning analytics (LA) has become a key area of study in educology, where it could assist in customising teaching and learning. Accordingly, it is precisely this data analysis technique that is used in a sensor—AnalyTIC—designed to identify students who are at risk of failing a course, and to prompt subsequent tutoring. This instrument provides the teacher and the student with the necessary information to evaluate academic performance by using a risk assessment matrix; the teacher can then customise any tutoring for a student having problems, as well as adapt the course contents. The sensor was validated in a study involving 39 students in the first term of the Environmental Engineering program at the Cooperative University of Colombia. Participants were all enrolled in an Algorithms course. Our findings led us to assert that it is vital to identify struggling students so that teachers can take corrective measures. The sensor was initially created based on the theoretical structure of the processes and/or phases of LA. A virtual classroom was built after these phases were identified, and the tool for applying the phases was then developed. After the tool was validated, it was established that students’ educational experiences are more dynamic when teachers have sufficient information for decision-making, and that tutoring and content adaptation boost the students’ academic performance.

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“…Learning analytics have been applied to study and visualize the relationship between student activity and performance in online-based university-level courses during the last decade (Dyckhoff, Zielke, Bültmann, Chatti, & Schroeder, 2012; Ferguson, 2012; Gunn, 2014; Nieto Acevedo & Montenegro Maran, 2015; Retalis, Papasalouros, Psaromiligkos, Siscos, & Kargidis, 2006; Scanlon, McAndrew, & O'Shea, 2015). The authors of 11 relevant studies published in peer-reviewed scholarly journals all found some benefits, but they also cited many problems when trying to assess student learning through combinations of learning analytics, learning management system (LMS) activity data logs, and graded performance results (Agudo-Peregrina, Iglesias-Pradas, Conde-Gonzalez, & Hernandez-Garcia, 2014; Fidalgo-Blanco, Sein-Echaluce, Garcia-Peealvo, & Conde, 2015; Gomez-Aguilar, Hernandez-Garcia, Garcia-Pealvo, & Theren, 2015; Iglesias-Pradas, Ruiz-de-Azcarate, & Agudo-Peregrina, 2015; Nieto Acevedo & Montenegro Maran, 2015; Reyes, 2015; Ruiparez-Valiente, Mua-Merino, Leony, & Delgado Kloos, 2015; Scheffel, Drachsler, Stoyanov, & Specht, 2014; Xing, Guo, Petakovic, & Goggins, 2015; Yahya, Messoussi, & Touahni, 2015).…”

Section: Introductionmentioning

confidence: 99%

Do the Critical Success Factors From Learning Analytics Predict Student Outcomes?

Strang

2016

Journal of Educational Technology Systems

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This article starts with a detailed literature review of recent studies that focused on using learning analytics software or learning management system data to determine the nature of any relationships between online student activity and their academic outcomes within university-level business courses. The article then describes how data was collected from an online course in Moodle learning management system and the student test scores are compared with the engagement learning analytics indicators to measure the strength and predictive nature the hypothesized relationships. The results were less than definitive and in some ways even puzzling. Despite the paucity of positive results, the findings indicated there was very little correlation between student online practices and their academic outcomes in a qualitative business course. On the other hand, several explanations were offered to account for these results, including small sample size, the specialized nature of the course, and the likelihood that students were downloading the material instead of interacting online. The article closes by suggesting directions for future learning analytics research in higher education.

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A proposal to a decision support system with learning analytics

Cited by 14 publications

References 13 publications

Can online student performance be forecasted by learning analytics?

Can online student performance be forecasted by learning analytics?

Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring

Do the Critical Success Factors From Learning Analytics Predict Student Outcomes?

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