A learning analytics approach: Using online weekly student engagement data to make predictions on student performance

Umer, Rahila; Sušnjak, Teo; Mathrani, Anuradha; Suriadi, Suriadi

doi:10.1109/icecube.2018.8610959

Cited by 16 publications

(16 citation statements)

References 4 publications

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“…However, results have shown that there is no improvement in the performance of the models using those three dimensions: cognitive, social and teaching presences. Because of that, one can assume that the simple counting of interactions can be used to generate predictive models, corroborating with previous work [59]. This contradicts the findings of other authors, such as Conijn et al [37] that say that predictive models cannot be generalized only by the LMS data logs and additional data sources are needed.…”

Section: Discussionsupporting

confidence: 74%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

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Section: Discussionsupporting

confidence: 74%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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“…The line separating both categories is sometimes very thin, as some of the predictors are able to offer results using only data available early in a course, which would easily allow them to serve as basis for an EWS. Examples of these situations can be observed in the works published by Thompson et al [11], Umer et al [13], and Hirose [15].…”

Section: Discussionmentioning

confidence: 93%

“…Umer et al attempted to estimate the earliest possible time at which a reliable prediction of the students' final performance in a course could be made [13]. This study involved 99 students enrolled in a 16-week-long introductory mathematics course taught at an Australian university.…”

Section: Predictorsmentioning

confidence: 99%

Systematic Literature Review of Predictive Analysis Tools in Higher Education

et al. 2019

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The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.

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“…Three different machine learning [9] algorithms were incorporated to predict the test performance of the learners. They had used six variables to trace the history for the learning.…”

Section: Literature Surveymentioning

confidence: 99%

“…Technology acceptance model designed by them had yielded a fruitful measure in 21st century skills of the students. Rahila et al [9] depicted a model to predict the growth of learner's performance by assessing the attributes of the learners in the weekly assessments. Ananthi and Nazreen [10] illustrated the user's experience in learning management systems.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model to predict the Industry Readiness of Engineering Students Community

Murugesan¹,

Ramasamy

Ashok³

et al. 2021

ITM Web Conf.

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Industry readiness of Engineering students community is a big challenge in the recent campus recruitments. 21st century skills are completely mapped with the technical and non – technical knowledge background of the engineering graduates. In this paper the work narrated the process of identifying the parameters for skill assessment of the candidates and derived a learner model using deep learning framework. Further the model can be used to predict the employability readiness of candidates.

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A learning analytics approach: Using online weekly student engagement data to make predictions on student performance

Cited by 16 publications

References 4 publications

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Systematic Literature Review of Predictive Analysis Tools in Higher Education

A Deep Learning Model to predict the Industry Readiness of Engineering Students Community

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