LEARNING ANALYTICS IN HUMAN HISTOLOGY REVEALS DIFFERENT STUDENT’ CLUSTERS AND DIFFERENT ACADEMIC PERFORMANCE

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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Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs

Interactive Learning Environments ◽

10.1080/10494820.2019.1636086 ◽

2019 ◽

Vol 28 (2) ◽

pp. 206-230 ◽

Cited By ~ 4

Author(s):

Anna Y. Q. Huang ◽

Owen H. T. Lu ◽

Jeff C. H. Huang ◽

C. J. Yin ◽

Stephen J. H. Yang

Keyword(s):

Big Data ◽

Academic Performance ◽

Learning Analytics ◽

Classification Methods ◽

Learning Logs

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Smart Learning Analytics: Student Academic Performance Data Representation, Processing and Prediction

Smart Education and e-Learning 2020 - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-15-5584-8_1 ◽

2020 ◽

pp. 3-18

Author(s):

Vladimir L. Uskov ◽

Jeffrey P. Bakken ◽

Kaustubh Gayke ◽

Juveriya Fatima ◽

Brandon Galloway ◽

...

Keyword(s):

Academic Performance ◽

Learning Analytics ◽

Data Representation ◽

Performance Data ◽

Smart Learning ◽

Student Academic Performance

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Curricular Design Analysis: A Data-Driven Perspective

Journal of Learning Analytics ◽

10.18608/jla.2014.13.6 ◽

2014 ◽

Vol 1 (3) ◽

pp. 84-119 ◽

Cited By ~ 9

Author(s):

Gonzalo Mendez ◽

Xavier Ochoa ◽

Katherine Chiluiza ◽

Bram De Wever

Keyword(s):

Academic Performance ◽

Computer Science ◽

Learning Process ◽

Learning Analytics ◽

Data Driven ◽

Educational Institutions ◽

Science Program ◽

Curricular Design ◽

Micro Level ◽

Course Difficulty

Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of Learning Analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

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A Review of Psychometric Data Analysis and Applications in Modelling of Academic Achievement in Tertiary Education

Journal of Learning Analytics ◽

10.18608/jla.2014.11.5 ◽

2014 ◽

Vol 1 (1) ◽

pp. 75-106 ◽

Cited By ~ 19

Author(s):

Geraldine Gray

Keyword(s):

Academic Performance ◽

Data Analysis ◽

Learning Strategies ◽

Learning Process ◽

Learning Analytics ◽

Tertiary Education ◽

Constant Variance ◽

Range Type ◽

College Participation ◽

Motivation And Learning

Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and therefore mitigate these challenges. The outcome from such data analysis will be dependent on the range, type, and quality of available data and the type of analysis performed. This study reviewed factors that could be used to predict academic performance, but which are currently not systematically measured in tertiary education. It focused on psychometric factors of ability, personality, motivation, and learning strategies. Their respective relationships with academic performance are enumerated and discussed. A case is made for their increased use in learning analytics to enhance the performance of existing student models. It is noted that lack of independence, linear additivity, and constant variance in the relationships between psychometric factors and academic performance suggests increasing relevance of data mining techniques, which could be used to provide useful insights on the role of such factors in the modelling of learning process.

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Understanding Student Behavior in a Flipped Classroom: Interpreting Learning Analytics Data in the Veterinary Pre-Clinical Sciences

Education Sciences ◽

10.3390/educsci10100260 ◽

2020 ◽

Vol 10 (10) ◽

pp. 260

Author(s):

Laura Dooley ◽

Nikolas Makasis

Keyword(s):

Higher Education ◽

Academic Performance ◽

Learning Analytics ◽

Student Behavior ◽

Flipped Classroom ◽

Learning Experience ◽

Individual Student ◽

Study Materials ◽

Student Access ◽

Access Patterns

The flipped classroom has been increasingly employed as a pedagogical strategy in the higher education classroom. This approach commonly involves pre-class learning activities that are delivered online through learning management systems that collect learning analytics data on student access patterns. This study sought to utilize learning analytics data to understand student learning behavior in a flipped classroom. The data analyzed three key parameters; the number of online study sessions for each individual student, the size of the sessions (number of topics covered), and the first time they accessed their materials relative to the relevant class date. The relationship between these parameters and academic performance was also explored. The study revealed patterns of student access changed throughout the course period, and most students did access their study materials before the relevant classroom session. Using k-means clustering as the algorithm, consistent early access to learning materials was associated with improved academic performance in this context. Insights derived from this study informed iterative improvements to the learning design of the course. Similar analyses could be applied to other higher education learning contexts as a feedback tool for educators seeking to improve the online learning experience of their students.

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