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.

scholarly journals The Transition to STEM Higher Education: Policy Recommendation – Conclusions of the readySTEMgo-Project

2018 ◽  
Vol 8 (2) ◽  
pp. 10
Author(s):  
Greet Langie ◽  
Maarten Pinxten
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Higher Education Policy ◽  
Early Stage ◽  
At Risk Students ◽  
Retention Rates ◽  
First Year ◽  
Policy Recommendations ◽  
Policy Recommendation ◽  
Key Skills

For Europe to remain at the forefront of scientific and technological devel-opment, the current shortage of persons trained in these fields at secondary and higher education has to be overcome. The readySTEMgo project aims to improve the retention rates of higher education STEM programmes by the identification of at-risk students in an early stage. We successfully identified a number of key skills that are essential for first-year achievement in a STEM programme. Additionally, we investigated which intervention tools can support at-risk students and evaluated their effectiveness. Based on the output of this research project four policy recommendations are formulated.

scholarly journals Redesigning a First Year Physiology Course using Learning Analytics to Improve Student Performance

2021 ◽  
Vol 3 (1) ◽  
pp. 4
Author(s):  
Mark T. Williams ◽  
Lesley Jan Lluka ◽  
Prasad Chunduri
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Student Learning ◽  
Student Performance ◽  
Statistical Models ◽  
Learning Analytics ◽  
First Year ◽  
Learning Context ◽  
Assessment Tasks ◽  
Course Outcomes

Learning analytics (LA), a fast emerging concept in higher education, is used to understand and optimize the student learning process and the envi-ronment in which it occurs. Knowledge obtained from the LA paradigm is often utilized to construct statistical models aimed at identifying students who are at risk of failing the unit/course, and to subsequently design inter-ventions that are targeted towards improving the course outcomes for these students. In previous studies, models were constructed using a wide variety of variables, but emerging evidence suggests that the models constructed us-ing course-specific variables are more accurate, and provide a better under-standing of the learning context. For our current study, student performance in the various course assessment tasks was used as a basis for the predictive models and future intervention design, as they are conventionally used to evaluate student learning outcomes and the degree to which the various course learning objectives are met. Further, students in our course are pri-marily first-year university students, who are still unfamiliar with the learning and assessment context of higher education, and this prevents them from adequately preparing for the tasks, and consequently reduces their course performance and outcome. We first constructed statistical models that would be used to identify students who are at risk of failing the course and to identify assessment tasks that students in our course find challeng-ing, as a guide for the design of future interventional activities. Every con-structed predictive model had an excellent capacity to discriminate between students who passed the course and those who failed. Analysis revealed that not only at-risk students, but the whole cohort, would benefit from in-terventions improving their conceptual understanding and ability to con-struct high-scoring answers to Short Answer Questions.

Increasing Academic Success Among Disadvantaged, At-Risk Students: The U-Pace Model

2009 ◽  
Author(s):  
Jessica Barnack ◽  
Raymond Fleming ◽  
Rodney Swain ◽  
Laura Pedrick ◽  
Diane M. Reddy
Keyword(s):  
At Risk ◽  
Academic Success ◽  
At Risk Students

Negotiating the boundaries between the formal and the informal: An experienced teacher's reflective adaptations of informal learning in a keyboard class for at-risk students

2016 ◽  
Vol 33 (3) ◽  
pp. 309-326 ◽  
Author(s):  
Pamela Costes-Onishi
Keyword(s):  
At Risk ◽  
Informal Learning ◽  
Student Learning ◽  
At Risk Students ◽  
Experienced Teachers ◽  
Formal And Informal Learning ◽  
Pedagogical Approaches

The objective of this study is to address the important questions raised in literature on the intersections between formal and informal learning. Specifically, this will be discussed within the concept of ‘productive dissonance’ and the pedagogical tensions that arise in the effort of experienced teachers to transition from the formal to the informal. This case study discusses the issues that ensue when strict demarcations between formal and informal are perceived, and demonstrates that the former is vital to the facilitation of the latter. The blurring of formal and informal pedagogical approaches has shown that the concept of ‘critical musicality’ becomes more apparent in student learning and that engagement increases especially among at-risk students.

scholarly journals Using Decision Trees and Random Forest Algorithms to Predict and Determine Factors Contributing to First-Year University Students’ Learning Performance

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 318
Author(s):  
Thao-Trang Huynh-Cam ◽  
Long-Sheng Chen ◽  
Huynh Le
Keyword(s):  
Random Forest ◽  
Performance Prediction ◽  
Prediction Models ◽  
Family Background ◽  
Educational Practice ◽  
Poor Performance ◽  
Learning Performance ◽  
First Year ◽  
First Year Students ◽  
Early Performance

First-year students’ learning performance has received much attention in educational practice and theory. Previous works used some variables, which should be obtained during the course or in the progress of the semester through questionnaire surveys and interviews, to build prediction models. These models cannot provide enough timely support for the poor performance students, caused by economic factors. Therefore, other variables are needed that allow us to reach prediction results earlier. This study attempts to use family background variables that can be obtained prior to the start of the semester to build learning performance prediction models of freshmen using random forest (RF), C5.0, CART, and multilayer perceptron (MLP) algorithms. The real sample of 2407 freshmen who enrolled in 12 departments of a Taiwan vocational university will be employed. The experimental results showed that CART outperforms C5.0, RF, and MLP algorithms. The most important features were mother’s occupations, department, father’s occupations, main source of living expenses, and admission status. The extracted knowledge rules are expected to be indicators for students’ early performance prediction so that strategic intervention can be planned before students begin the semester.

ENRICHMENT FOR ACADEMIC SUCCESS: HELPING AT-RISK STUDENTS

1990 ◽  
Vol 15 (6) ◽  
pp. 33-37 ◽  
Author(s):  
ARTHUR REE CAMPBELL ◽  
SANDRA M. DAVIS
Keyword(s):  
At Risk ◽  
Academic Success ◽  
At Risk Students

scholarly journals Using Heart Rate to Predict Students’ Academic Performance

2019 ◽  
Vol 8 (3) ◽  
pp. 5916-5920
Keyword(s):  
At Risk ◽  
Heart Rate ◽  
Academic Performance ◽  
Prediction Models ◽  
At Risk Students ◽  
Rate Data ◽  
Threshold Values ◽  
Heart Rate Data ◽  
Rate Fluctuation ◽  
Remedial Intervention

Timeliness was a missing factor in many studies on Academic Performance Prediction to identify at-risk students. This study embarked on a search to evaluate the feasibility of predicting students’ performance based on heart rate data collected during classes. This dimension of data was collected in the first four weeks after semester commencement to validate accurate prediction that will enable educationists to introduce remedial intervention to at-risk students. Another aim of this study is to determine the best threshold values for the different types of heart rate fluctuations that can be used in predicting academic achievements. The threshold values were tested further to verify whether the prediction model for individual course or combined courses was more accurate. Results revealed that heart rate data alone can achieve a maximum prediction accuracy of 88% and recall of 100%. Threshold values calculated in derived heart rate fluctuation types produces the best results. Prediction models for individual courses outperform the model using average threshold values of all courses.

Sign in / Sign up

Export Citation Format

Share Document