scholarly journals Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative

2014 ◽  
Vol 1 (1) ◽  
pp. 6-47 ◽  
Author(s):  
Sandeep M. Jayaprakash ◽  
Erik W. Moody ◽  
Eitel J.M. Lauría ◽  
James R. Regan ◽  
Joshua D. Baron
Keyword(s):  
At Risk ◽  
Learning Analytics ◽  
At Risk Students ◽  
Predictive Performance ◽  
Scaling Up ◽  
Academic Risk ◽  
Academic Analytics ◽  
Grant Program ◽  
Pilot Programs ◽  
Goals And Objectives

The Open Academic Analytics Initiative (OAAI) is a collaborative, multi-year grant program aimed at researching issues related to the scaling up of learning analytics technologies and solutions across all of higher education. The paper describes the goals and objectives of the OAAI, depicts the process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at-risk students.

scholarly journals Monitoring Students at the University: Design and Application of a Moodle Plugin

2020 ◽  
Vol 10 (10) ◽  
pp. 3469 ◽  
Author(s):  
María Consuelo Sáiz-Manzanares ◽  
Raúl Marticorena-Sánchez ◽  
César Ignacio García-Osorio
Keyword(s):  
At Risk ◽  
Early Detection ◽  
At Risk Students ◽  
Machine Learning Techniques ◽  
Dropout Rates ◽  
Dynamic Learning ◽  
Academic Risk ◽  
University Environment ◽  
Student Dropout ◽  
The University

Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.

Using Learning Analytics to Predict At-Risk Students in Online Graduate Public Affairs and Administration Education

2015 ◽  
Vol 21 (2) ◽  
pp. 247-262 ◽  
Author(s):  
Jay Bainbridge ◽  
James Melitski ◽  
Anne Zahradnik ◽  
Eitel J. M. Lauría ◽  
Sandeep Jayaprakash ◽  
...  
Keyword(s):  
At Risk ◽  
Learning Analytics ◽  
At Risk Students ◽  
Public Affairs

Adopting Learning Analytics in a First-Year Veterinarian Professional Program: What We Could Know in Advance about Student Learning Progress

2021 ◽  
Vol 48 (6) ◽  
pp. 720-728
Author(s):  
Wenting Weng ◽  
Nicola L. Ritter ◽  
Karen Cornell ◽  
Molly Gonzales
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Student Learning ◽  
Learning Analytics ◽  
Prediction Models ◽  
Early Stage ◽  
At Risk Students ◽  
Learning Performance ◽  
First Year ◽  
Actual Performance

Over the past decade, the field of education has seen stark changes in the way that data are collected and leveraged to support high-stakes decision-making. Utilizing big data as a meaningful lens to inform teaching and learning can increase academic success. Data-driven research has been conducted to understand student learning performance, such as predicting at-risk students at an early stage and recommending tailored interventions to support services. However, few studies in veterinary education have adopted Learning Analytics. This article examines the adoption of Learning Analytics by using the retrospective data from the first-year professional Doctor of Veterinary Medicine program. The article gives detailed examples of predicting six courses from week 0 (i.e., before the classes started) to week 14 in the semester of Spring 2018. The weekly models for each course showed the change of prediction results as well as the comparison between the prediction results and students’ actual performance. From the prediction models, at-risk students were successfully identified at the early stage, which would help inform instructors to pay more attention to them at this point.

scholarly journals A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course

2020 ◽  
Vol 10 (11) ◽  
pp. 3998 ◽  
Author(s):  
Emanuel Marques Queiroga ◽  
João Ladislau Lopes ◽  
Kristofer Kappel ◽  
Marilton Aguiar ◽  
Ricardo Matsumura Araújo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
At Risk ◽  
Learning Analytics ◽  
Prediction Models ◽  
Demographic Data ◽  
Characteristic Curve ◽  
At Risk Students ◽  
Virtual Learning Environment ◽  
Learning Context ◽  
Student Dropout

Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students’ interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin’s theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques.

Combining Academics and Social Engagement: A Major-Specific Early Alert Method to Counter Student Attrition in Science, Technology, Engineering, and Mathematics

2018 ◽  
Vol 22 (4) ◽  
pp. 611-626
Author(s):  
Andrew J. Sage ◽  
Cinzia Cervato ◽  
Ulrike Genschel ◽  
Craig A. Ogilvie
Keyword(s):  
At Risk ◽  
Social Engagement ◽  
Standardized Test ◽  
At Risk Students ◽  
Predictive Performance ◽  
Stem Retention ◽  
Science Technology ◽  
And Mathematics ◽  
Institutional Retention ◽  
Noncognitive Factors

Students are most likely to leave science, technology, engineering, and mathematics (STEM) majors during their first year of college. We developed an analytic approach using random forests to identify at-risk students. This method is deployable midway through the first semester and accounts for academic preparation, early engagement in university life, and performance on midterm exams. By accounting for cognitive and noncognitive factors, our method achieves stronger predictive performance than would be possible using cognitive or noncognitive factors alone. We show that it is more difficult to predict whether students will leave STEM than whether they will leave the institution. More factors contribute to STEM retention than to institutional retention. Early academic performance is the strongest predictor of STEM and institution retention. Social engagement is more predictive of institutional retention, while standardized test scores, goals, and interests are stronger predictors of STEM retention. Our approach assists universities to efficiently identify at-risk students and boost STEM retention.

Impact of teachers’ grading policy on the identification of at-risk students in learning analytics

2021 ◽  
Vol 163 ◽  
pp. 104109
Author(s):  
Owen H.T. Lu ◽  
Anna Y.Q. Huang ◽  
Stephen J.H. Yang
Keyword(s):  
At Risk ◽  
Learning Analytics ◽  
At Risk Students ◽  
Grading Policy

scholarly journals Direct Measure Assessment of Learning Outcome–Driven Proactive Advising for Academically At-Risk Students

2019 ◽  
Vol 39 (1) ◽  
pp. 60-76 ◽  
Author(s):  
Stephanie Kraft-Terry ◽  
Cheri Kau
Keyword(s):  
At Risk ◽  
Student Learning ◽  
Curriculum Design ◽  
At Risk Students ◽  
Academic Risk ◽  
Backward Design ◽  
Assessment Plans ◽  
Multiple Assessment ◽  
Improve Student Learning ◽  
Academically At Risk

Creating advising curricula through backward design ensures that learning objectives remain central to the process and enables those in advising units to design comprehensive assessment plans for continued curricular improvement. By incorporating measures to observe student learning directly, advisors can evaluate their curriculum objectively to ensure students achieve desired learning outcomes. An advising unit created a proactive advising curriculum for academically at-risk students through backward design that includes multiple assessment measures. Students in four categories of academic risk were targeted for intervention. Through the evaluation of direct-learning evidence gathered through assessment, the advising unit improved the advising curriculum, showing the process for intentional curriculum design and assessment to improve student learning.

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