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.

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 Personality traits and academic performance: Correcting self-assessed traits with vignettes

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248629
Author(s):  
Johan Coenen ◽  
Bart H. H. Golsteyn ◽  
Tom Stolp ◽  
Dirk Tempelaar
Keyword(s):  
Higher Education ◽  
Personality Traits ◽  
Student Performance ◽  
Cognitive Skills ◽  
Risk Preference ◽  
Emotional Stability ◽  
First Year ◽  
First Year Students ◽  
Significant Relationships ◽  
Point Estimates

In this study, we investigate whether Conscientiousness, Emotional Stability and Risk Preference relate to student performance in higher education. We employ anchoring vignettes to correct for heterogeneous scale use in these non-cognitive skills. Our data are gathered among first-year students at a Dutch university. The results show that Conscientiousness is positively related to student performance, but the estimates are strongly biased upward if we use the uncorrected variables. We do not find significant relationships for Emotional Stability but find that the point estimates are larger when using the uncorrected variables. Measured Risk Preference is negatively related to student performance, yet this is fully explained by heterogeneous scale use. These results indicate the importance of using more objective measurements of personality traits.

scholarly journals LEARNING ANALYTICS FOR WHOM? A REFLEXION ON THE RETRIEVAL OF LEARNING INFORMATION BY THE STUDENT

2021 ◽  
Vol 9 (2) ◽  
pp. 282-294
Author(s):  
Darcio Costa Nogueira Junior ◽  
Isadora Valle Sousa ◽  
Frederico Cordeiro Martins ◽  
Marta Macedo Kerr Pinheiro
Keyword(s):  
Student Performance ◽  
Learning Analytics ◽  
Personalized Learning ◽  
Data Availability ◽  
Learning Context ◽  
Student Autonomy ◽  
Information User ◽  
Narrative Literature ◽  
Qualitative Nature ◽  
Narrative Literature Review

The present work aims at addressing how the use of Learning Analytics (LA) has enabled the retrieval of learning information by the student oneself, by analyzing data availability, self-management and student autonomy in learning processes inside and outside virtual environments. The bibliographic research conducted had a qualitative nature and consisted of a narrative literature review anchored in the theoretical foundations of information (information retrieval and representation) and Learning Analytics. Two relevant user case studies that dealt with LA were selected from the researched articles - the first analyzed the user approach in an adapted learning context with LA whereas the second analyzed the user approach in a personalized learning context with LA. One concluded that the student, as an information user, still has little access to an effective retrieval of what was consolidated throughout one’s own learning process. Besides, in relation to the effectiveness of LA, in the context of adapted and personalized learning, there was a perceived increase in student performance with regard to the use of activities and tasks.

scholarly journals Predictive learning analytics and the creation of emotionally adaptive learning environments in higher education institutions: a study of students' affect responses

2021 ◽  
Vol 38 (2) ◽  
pp. 243-257
Author(s):  
Paul Joseph-Richard ◽  
James Uhomoibhi ◽  
Andrew Jaffrey
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Well Being ◽  
Small Sample ◽  
Small Scale ◽  
Affective States ◽  
Content Type ◽  
Predictive Learning

PurposeThe aims of this study are to examine affective responses of university students when viewing their own predictive learning analytics (PLA) dashboards, and to analyse how those responses are perceived to affect their self-regulated learning behaviour.Design/methodology/approachA total of 42 Northern Irish students were shown their own predicted status of academic achievement on a dashboard. A list of emotions along with definitions was provided and the respondents were instructed to verbalise them during the experience. Post-hoc walk-through conversations with participants further clarified their responses. Content analysis methods were used to categorise response patterns.FindingsThere is a significant variation in ways students respond to the predictions: they were curious and motivated, comforted and sceptical, confused and fearful and not interested and doubting the accuracy of predictions. The authors show that not all PLA-triggered affective states motivate students to act in desirable and productive ways.Research limitations/implicationsThis small-scale exploratory study was conducted in one higher education institution with a relatively small sample of students in one discipline. In addition to the many different categories of students included in the study, specific efforts were made to include “at-risk” students. However, none responded. A larger sample from a multi-disciplinary background that includes those who are categorised as “at-risk” could further enhance the understanding.Practical implicationsThe authors provide mixed evidence for students' openness to learn from predictive learning analytics scores. The implications of our study are not straightforward, except to proceed with caution, valuing benefits while ensuring that students' emotional well-being is protected through a mindful implementation of PLA systems.Social implicationsUnderstanding students' affect responses contributes to the quality of student support in higher education institutions. In the current era on online learning and increasing adaptation to living and learning online, the findings allow for the development of appropriate strategies for implementing affect-aware predictive learning analytics (PLA) systems.Originality/valueThe current study is unique in its research context, and in its examination of immediate affective states experienced by students who viewed their predicted scores, based on their own dynamic learning data, in their home institution. It brings out the complexities involved in implementing student-facing PLA dashboards in higher education institutions.

scholarly journals A Resource-Constrained Approach to Implementing Analytics in an Institution of Higher Education: An Experience Report

2014 ◽  
Vol 1 (1) ◽  
pp. 129-139 ◽  
Author(s):  
John P Buerck
Keyword(s):  
Higher Education ◽  
Student Performance ◽  
Learning Analytics ◽  
Higher Learning ◽  
Academic Environment ◽  
Academic Institutions ◽  
Successful Completion ◽  
Academic Analytics ◽  
Institutions Of Higher Learning ◽  
Improving Student Performance

Academic analytics and learning analytics have been increasingly adopted by academic institutions of higher learning for improving student performance and retention. While several studies have reported the implementation details and the successes of specific analytics initiatives, relatively fewer studies exist in literature that describe the possible constraints that can preclude an academic or learning analytics initiative from succeeding fully, meeting the criteria of success as defined by the stakeholders affected by such initiatives. Our aim in this article is to describe the constraints that precluded a successful completion of our analytics initiative and how we re-envisioned our approach and scope to achieve our primary goals while operating within the constraints and tools associated with our academic environment.

Learning Analytics: The Emperor’s New Clothes?

2017 ◽  
Vol 3 (1) ◽  
pp. 76
Author(s):  
Sue Milward
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Retention ◽  
Student Learning ◽  
Learning Analytics

Learning Analytics is promising to deliver the power of big data to Higher Education. By extracting meaning from the myriad of data held against a student, Learning Analytics promises to improve student retention and attainment. However, there are challenges to be overcome before the reality can live up to the promises.  

scholarly journals The use of human resources literature regarding the relationship between affect and student academic performance

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Chris W. Callaghan ◽  
Elmarie Papageorgiou
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Academic Performance ◽  
Negative Affect ◽  
Human Resources ◽  
Student Performance ◽  
Individual Performance ◽  
Test Theory ◽  
First Year ◽  
Specific Potential

Orientation: In human resources literature affect, or affectivity, has been identified as contributing, either negatively or positively, to different forms of performance in a range of different contexts.Research purpose: The aim of the study was to empirically test theory that predicts that affect can influence performance; in this case the academic performance of students in the South African higher education context.Motivation for the study: Human resources job performance theory seems to offer important insights when extended into other contexts of individual performance. The specific potential influence of affect on student performance is unclear in this context.Research design, approach and method: A non-probability comprehensive sample of all students registered for first-year accountancy (n = 719) was used. Confirmatory factor analysis, exploratory factor analysis and bivariate tests of association were used to empirically test theory predicting relationships between affect and student academic performance.Main findings: In general the findings support the predications derived from affect theory, that negative affect is negatively associated with student performance and that positive affect is positively associated with student performance. Yet, the results suggest that affect might not, in this context, reflect the two-dimensional theoretical structure. In particular, negative affectivity might better be considered as a three-dimensioned construct.Practical/managerial implications: These results suggest that proactive measures may need to be taken by higher education institutions to support first-year students affectively. Student advisors or counsellors should be appointed, with a specific focus on providing support for student anxiety and other contextual frustrations to which individuals with higher levels of negative affect might be particularly vulnerable.Contribution: These findings provide new insights into the importance of extending human resource theory into different contexts. Knowledge of the specific potential constraints posed by affect to student performance is provided.

scholarly journals The Contribution of Learning and Mental Health Variables in First-Year Students' Profiles

2021 ◽  
Vol 12 ◽  
Author(s):  
Fotios S. Milienos ◽  
Christos Rentzios ◽  
Leen Catrysse ◽  
David Gijbels ◽  
Sofia Mastrokoukou ◽  
...  
Keyword(s):  
Mental Health ◽  
Higher Education ◽  
At Risk ◽  
Undergraduate Students ◽  
Emotion Dysregulation ◽  
Educational Institution ◽  
First Year ◽  
International Studies ◽  
First Year Students ◽  
Student Profiles

International studies focus on the successful transition into higher education, which is considered crucial for both the students and the educational institution in the context of students' learning and adjustment in higher education. The aim of the current study was to identify student profiles that include cognitive, metacognitive, and motivational aspects of learning, but also aspects of resilience, emotion dysregulation, and anxiety. The sample consists of 316 Greek undergraduate students (18.7% males and 81.3% females). The results showed four different (meta)-cognitive-emotional learner profiles: the emotionally stable and highly adaptive learner; the emotionally dysregulated and at risk learner; the emotionally dysregulated and highly adaptive learner; the emotionally stable and at risk learner. Emotionally dysregulated and at risk learner has a lower GPA than the emotional stable and highly adaptive learner, the emotionally dysregulated and highly adaptive learner and the emotionally stable and at risk learner.

scholarly journals Implementing first-year assessment principles: An analysis of selected scholarly literature

2018 ◽  
Vol 9 (2) ◽  
pp. 25-38
Author(s):  
Theda Thomas
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Current Literature ◽  
Future Research ◽  
First Year ◽  
Assessment Practices ◽  
First Year Students ◽  
Academic Transition ◽  
Scholarly Literature ◽  
Assessment Tasks

Assessment plays an important role in students’ learning as students often frame their learning around their assessment tasks. Well-designed assessment can be used to facilitate first-year students making their social and academic transition to university. In 2009, Professor David Nicol prepared a framework for first-year assessment practices that included 12 principles. In this study, these principles were revisited and used to analyse papers from 2013 to 2016 in the journals: ‘Assessment & Evaluation in Higher Education’, ‘The International Journal of First Year in Higher Education’ and ‘Student Success’. The purpose of the study was to determine how current literature addresses Nicol’s first-year assessment principles, whether there were any issues in implementing them and whether anything new is emerging in the field. Based on this analysis, proposals are made for modifying the principles and recommendations are made for future research.

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