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

2019 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
Fredys Simanca ◽  
Rubén González Crespo ◽  
Luis Rodríguez-Baena ◽  
Daniel Burgos
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Virtual Classroom ◽  
Sufficient Information ◽  
Students At Risk ◽  
Theoretical Structure ◽  
Analysis Technique ◽  
Assessment Matrix

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.

scholarly journals Effectiveness of Using Appropriate Blended Learning Tools for Teaching and Learning Computer Programming Related Courses

2020 ◽  
Vol 1 (1) ◽  
pp. 56-63
Author(s):  
Emerson Raja Joseph ◽  
Md Jakir Hoseen ◽  
Fazly Salleh ◽  
Lim Way Soong
Keyword(s):  
Academic Performance ◽  
Blended Learning ◽  
Teaching And Learning ◽  
Online Survey ◽  
Virtual Classroom ◽  
Discussion Board ◽  
Digital Tools ◽  
Learning Tools ◽  
Computer Based ◽  
Student Centred Learning

With the advent of technology there are plenty of blended learning tools available for us to use in teaching and training activity. Selecting appropriate tools for a particular category of students and the nature of the subject being taught is important to achieve better academic results. Hence, the objective of this research is to assess effectiveness of various blended learning tools and to find the appropriate tool for teaching a computer -based campus in Malaysia. This subject was delivered using four selected blended learning digital tools; ED puzzle virtual classroom videos, Home works on MMLS, MMLS online Quiz and MMLS discussion board, at the beginning of Trimester 2, 2018/2019. They were asked in the middle of the trimester to rate the usefulness of the four selected blended learning digital tools in a 5-point scale using an online survey. The analysis feedback shows that D puzzle virtual the trimester. The effectivenes when the achievements of the students in terms of their academic performance were compared with previous year. It clearly shows that the academic performance of the students of year 2019 is better than of students towards student centred learning.

scholarly journals Early Identification of Students at Risk for Poor Academic Performance in Clinical Clerkships

2000 ◽  
Vol 75 (Supplement) ◽  
pp. S78-S80 ◽  
Author(s):  
SCOTT A. FIELDS ◽  
CYNTHIA MORRIS ◽  
WILLIAM L. TOFFLER ◽  
EDWARD J. KEENAN
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Early Identification ◽  
Students At Risk ◽  
Poor Academic Performance ◽  
Clinical Clerkships

scholarly journals Modeling E-Behaviour, Personality and Academic Performance with Machine Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10546
Author(s):  
Serepu Bill-William Seota ◽  
Richard Klein ◽  
Terence van Zyl
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Academic Performance ◽  
Student Performance ◽  
Correlation Coefficients ◽  
Students At Risk ◽  
Intervention Process ◽  
Population Correlation ◽  
Online Behaviour ◽  
Personality Features

The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (p<0.05) population correlation coefficients for traits against grade—0.846 for Extraversion and 0.319 for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a classification accuracy (κ) of students at risk of 0.51. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education.

scholarly journals Identifying Students at Risk to Academic Dropout in Higher Education

2021 ◽  
Vol 11 (8) ◽  
pp. 427
Author(s):  
María Gómez Gallego ◽  
Alfonso Palazón Perez de los Cobos ◽  
Juan Cándido Gómez Gallego
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Learning Strategies ◽  
Cognitive Functions ◽  
Academic Failure ◽  
Dropout Rate ◽  
Cognitive Capacity ◽  
Students At Risk ◽  
Learning Skills ◽  
Increased Risk

A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption of strategies for preventing and reducing school dropout. In this research, cognitive functions and learning skills in 284 university students were evaluated. Academic performance predictors were identified, and conglomerates analysis was carried out to establish groups according to those variables. The stability and validity of the conglomerates were tested with discriminant analyzes and comparison tests. The variables associated significantly to academic performance were: attention, intelligence, motivation, metacognition and affective components. The conglomerate analysis suggested a three-group solution: (1) students with cognitive skills of moderate to high, but deficient learning strategies; (2) students with cognitive and learning capabilities of moderate to high; (3) students with cognitive functions low and moderate learning capacity. Students from groups 1 and 3 showed worse academic performance; 83.3% of students at risk of desertion belonged to such groups. Two groups of students have been identified with the highest risk of academic failure: those with poor cognitive capacity and those with bad learning skills.

scholarly journals About "Learning" and "Analytics"

2016 ◽  
Vol 3 (2) ◽  
pp. 1-5 ◽  
Author(s):  
Arnon Hershkovitz ◽  
Simon Knight ◽  
Shane Dawson ◽  
Jelena Jovanović ◽  
Dragan Gašević
Keyword(s):  
At Risk ◽  
Early Detection ◽  
21St Century ◽  
21St Century Skills ◽  
Learning Analytics ◽  
Tertiary Education ◽  
Editorial Team ◽  
Multimodal Learning ◽  
Students At Risk ◽  
Multimodal Learning Analytics

This issue of the Journal of Learning Analytics features three special sections that look into topics of learning analytics for 21st century skills, multimodal learning analytics, and sharing of datasets for learning analytics. The issue also features a paper that looks at models for early detection of students at risk in tertiary education. The editorial concludes with a summary of the changes in the editorial team of the journal.

scholarly journals Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education

2016 ◽  
Vol 3 (2) ◽  
pp. 330-372 ◽  
Author(s):  
Geraldine Gray ◽  
Colm McGuinness ◽  
Philip Owende ◽  
Markus Hofmann
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Tertiary Education ◽  
Early Stage ◽  
Self Regulation ◽  
Support Vector ◽  
First Year ◽  
Learning Approaches ◽  
Students At Risk ◽  
Learning Factor

This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year of study. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool administered during first-year student induction. Factors considered included prior academic performance, personality, motivation, self-regulation, learning approaches, age and gender.  Models were trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort. A comparison of eight classification algorithms found k-NN achieved best model accuracy (72%), but results from other models were similar, including ensembles (71%), support vector machine (70%) and a decision tree (70%). Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. This study indicated that early modelling of first year students yielded informative, generalisable models that identified students at risk of failing.

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