scholarly journals Predicting student performance in a blended learning environment using learning management system interaction data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kiran Fahd ◽  
Shah Jahan Miah ◽  
Khandakar Ahmed
Keyword(s):  
At Risk ◽  
Blended Learning ◽  
Student Performance ◽  
Well Being ◽  
Student Attrition ◽  
Pedagogical Practices ◽  
Students At Risk ◽  
Content Type ◽  
Learning Management ◽  
Predicting Student Performance

PurposeStudent attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.Design/methodology/approachThis study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.FindingsIdentifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.Originality/valueThe best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

Using the WHO–5 Well-Being Index to Identify College Students at Risk for Mental Health Problems

2017 ◽  
Vol 58 (1) ◽  
pp. 113-117 ◽  
Author(s):  
Andrew Downs ◽  
Laura A. Boucher ◽  
Duncan G. Campbell ◽  
Anita Polyakov
Keyword(s):  
Mental Health ◽  
College Students ◽  
At Risk ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Students At Risk

Drop-out in programming courses – prediction and prevention

2019 ◽  
Vol 12 (1) ◽  
pp. 124-136 ◽  
Author(s):  
Anja Hawlitschek ◽  
Veit Köppen ◽  
André Dietrich ◽  
Sebastian Zug
Keyword(s):  
At Risk ◽  
Correlation Analysis ◽  
Activity Patterns ◽  
Drop Out ◽  
Instructional Interventions ◽  
Learner Characteristics ◽  
Students At Risk ◽  
Content Type ◽  
Prediction And Prevention ◽  
Drop Outs

Purpose An ideal learning analytics tool for programming exercises performs the role of a lecturer who monitors the code development, provides customized support and identifies students at risk to drop out. But a reliable prediction and prevention of drop-out is difficult, due to the huge problem space in programming tasks and variety of solutions and programming strategies. The purpose of this paper is to tackle this problem by, first, identifying activity patterns that indicate students at risk; and, second, finding reasons behind specific activity pattern, for identification of instructional interventions that prevent drop-out. Design/methodology/approach The authors combine two investigation strategies: first, learning analytic techniques (decision trees) are applied on features gathered from students, while completing programming exercises, in order to classify predictors for drop-outs. Second, the authors determine cognitive, motivational and demographic learner characteristics based on a questionnaire. Finally, both parts are related with a correlation analysis. Findings It was possible to identify generic variables that could predict early and later drop-outs. For students who drop out early, the most relevant variable is the delay time between availability of the assignment and the first login. The correlation analysis indicates a relation with prior programming experience in years and job occupation per week. For students who drop out later in the course, the number of errors within the first assignment is the most relevant predictor, which correlates with prior programming skills. Originality/value The findings indicate a relation between activity patterns and learner characteristics. Based on the results, the authors deduce instructional interventions to support students and to prevent drop-outs.

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 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 Blended learning: how to flip the classroom at HEIs in Bangladesh?

2019 ◽  
Vol 13 (2) ◽  
pp. 228-242 ◽  
Author(s):  
Faieza Chowdhury
Keyword(s):  
Blended Learning ◽  
Flipped Classroom ◽  
Secondary Data ◽  
Well Being ◽  
Primary Data ◽  
Successful Implementation ◽  
Learning Program ◽  
Content Type ◽  
Learning Programs

Purpose E-learning is a very popular concept in the education sector today, and one of the best ways to implement this is through blended learning. However, the implementation of blended learning program at Higher Education Institutions (HEIs) is quite new in Bangladesh. The purpose of this paper is to explore the concept of blended learning, how to construct a blended learning program, the benefits of blended learning and some prerequisites to implement blended learning program successfully at HEIs in Bangladesh. Design/methodology/approach Nature of the study is explanatory, descriptive as well as evaluative. Primary data were collected through face-to-face interviews using structured questionnaire having both open- and close-ended questions including personal observations. Secondary data comprise relevant documents available from government agencies, archives, and library and research organizations. Findings By utilizing the blended learning tools, HEIs in Bangladesh can achieve radical improvements in education quality as well as in the accessibility and cost-effectiveness of learning programs. Moreover, any innovative educational reform will be successful only when it is fully accepted and adopted by all the key stakeholders: students, parents, teachers, academic administrators, researchers and policy makers. Practical implications Several practical solutions have been presented in this paper: how to create a blended learning program, how to overcome the obstacles for successful implementation of blended learning and how to create a flipped classroom with the aid of technology. Social implications A country’s soul and economic well-being depends to a large extent on the quality of their citizen’s education. Implementing innovative teaching programs within the education system will enhance the quality of education at HEIs in Bangladesh, creating more efficient labor force hence benefiting the overall society. Originality/value Originality in terms of exposing the hurdles that needs to be addressed for successful implementation of blended learning programs at HEIs in Bangladesh and providing an easy guideline to educators on how to create flipped classrooms.

International Students At-Risk

2020 ◽  
pp. 238-251
Author(s):  
John Cuzzocrea
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
International Students ◽  
International Student ◽  
Culture Shock ◽  
Well Being ◽  
Students At Risk ◽  
International Student Recruitment ◽  
General School ◽  
Mental Well Being

International student recruitment has grown in schools across Canada and has extended into the elementary and secondary school panels. These students have specific needs and challenges, which put them at greater risk in comparison to the general school population. International students, especially at first, may struggle with risk factors such as culture shock, homesickness, loneliness, and depression. It is important to understand these challenges to ensure the mental well-being of these students.

scholarly journals Identifying students at risk in academics

2017 ◽  
Vol 1 (1) ◽  
pp. 8-18
Author(s):  
Adam Christian Haupt ◽  
Jonathan Alt ◽  
Samuel Buttrey
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Regression Models ◽  
At Risk Students ◽  
Korean Language ◽  
Students At Risk ◽  
Content Type ◽  
Logistic Regression Models ◽  
Data Driven Approach ◽  
Defense Language Institute

Purpose This paper aims to use a data-driven approach to identify the factors and metrics that provide the best indicators of academic attrition in the Korean language program at the Defense Language Institute Foreign Language Center. Design methodology approach This research develops logistic regression models to aid in the identification of at-risk students in the Defense Language Institute’s Korean language school. Findings The results from this research demonstrates that this methodology can detect significant factors and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be detected using logistic regression models and stepwise regression. Originality value This research represents a real-world application of logistic regression modeling methods applied to the problem of identifying at-risk students for the purpose of academic intervention or other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of the problem and identify statistically significant predictors of student attrition that they believe can be converted into meaningful policy change.

Making connections – reducing loneliness and encouraging well-being

2015 ◽  
Vol 19 (4) ◽  
pp. 182-187
Author(s):  
Shelagh Marshall OBE ◽  
Janet Crampton
Keyword(s):  
At Risk ◽  
Low Cost ◽  
Well Being ◽  
Health Care Team ◽  
High Rate ◽  
Content Type ◽  
High Street ◽  
The Right ◽  
Rate Of Returns ◽  
Health And Well Being

Purpose – The purpose of this paper is to: first, report on a pilot; second, provide a further opportunity for a wider audience to be aware of the work carried out by the Age Action Alliance, Isolation and Loneliness Working Group to identify vulnerable people in the community. Third, to highlight the successful aspects of the project which could be used by other organisations seeking to reduce the effects of isolation and loneliness in the community. Links to the full report and the more detailed findings can be found at: www.ageactionalliance Design/methodology/approach – The main proposal was to test the most effective approach to identifying those at risk of loneliness, using pharmacists in two well-known “high street” pharmacies, through the use of a simple questionnaire that could be handed out to a target 100 customers at each pharmacy or health care team over a six-week period. Findings – A simple questionnaire proves to be successful and gets a good rate of return. The right partners are essential to bring effective results. Referrals were handled very professionally and people were helped to connect socially. Research limitations/implications – The sample was small but the authors achieved a relatively high rate of returns and, in consequence, a number of people were directly helped access the support, information and advice to enable them to feel less lonely. Practical implications – The planning and preparation for this project proved that all needed to be actively and continuously involved in the planning from the beginning. Furthermore in this project involving local pharmacies, the manager or lead pharmacist at a store need to lead and actively engage their staff in the aims and objectives of the project. Social implications – This project aimed to identify people at risk of loneliness and the potential adverse effect on their health and well-being. Anyone helped to avoid social isolation and loneliness is a success, and sometimes with relatively low cost but high-impact intervention. Originality/value – This project was conceived amongst partners and reflected the particular involvement of a “household name” pharmacy and recognition of its key role in identifying and accessing people who may be at risk of loneliness.

scholarly journals The “Supporting Kids, Avoiding Problems” (SKIP) study: relationships between school exclusion, psychopathology, development and attainment – a case control study

2016 ◽  
Vol 11 (2) ◽  
pp. 91-110 ◽  
Author(s):  
Claire Parker ◽  
Ruth Marlow ◽  
Marc Kastner ◽  
Felix May ◽  
Oana Mitrofan ◽  
...  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Normative Data ◽  
Well Being ◽  
Case Control ◽  
Content Type ◽  
Control Approach ◽  
Number Of Children ◽  
Behavioural Difficulties ◽  
Health And Well Being

Purpose – The purpose of this paper is to explore the association between children who are at risk of being or who have been excluded from school between the ages of 4 and 12 years and the role of psychopathology, development and attainment. Design/methodology/approach – A case-control approach was conducted. Cases were children who had been excluded from school compared to those who had no reported exclusions and normative data where possible. A range of measures were used to collect information from the parent, child and teacher on areas covering the child’s mental health and well-being. Findings – The findings showed the number of difficulties faced by children who are at risk of being or who have been excluded from school compared to gender- and age-matched controls and normative data increased. Behavioural difficulties were apparent in the majority of the cases and an alarming number of children reported self-harm. Interestingly nearly all the cases had recognised needs, but not all of them were accessing appropriate services. Practical implications – There have been a number of changes regarding the identification and support of children’s mental health and well-being. This study highlights gaps in resources and provision, particularly around behavioural difficulties for children who are presenting as not coping in school. Originality/value – The findings from the SKIP study indicate the complexities and compounded difficulties faced by children who are experiencing exclusion from school. By implementing a systematic group of assessments the study was able to identify these complexities of need across a vulnerable group of children.

scholarly journals Face-to-face delivery this week; online the next: a reflection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahesha Perera ◽  
Liz Rainsbury ◽  
Saman Bandara
Keyword(s):  
Online Learning ◽  
Student Engagement ◽  
Blended Learning ◽  
Positive Impact ◽  
Academic Staff ◽  
Well Being ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Content Type ◽  
Face To Face

Purpose The purpose of this paper is to reflect on the effects of online learning on student engagement as a result of a shift from face-to-face to online learning during the COVID-19 lockdown in New Zealand. Design/methodology/approach The reflection expresses the accounting lecturers’ observations and experiences of student engagement in online learning during the COVID-19 lockdown focussing on the three facets of student engagement; social presence, cognitive presence and teaching presence. Findings The focus on social and teaching presence in online learning by Unitec academic staff had a positive impact on cognitive presence as student course success rates and course ratings were similar to rates achieved from face-to-face delivery despite a rapid transition to online learning. Research limitations/implications This reflection is based on the experiences of three academic staff in one tertiary organisation. Practical implications The findings of this study can be helpful for tertiary institutions that are planning to adopt blended learning in the future. Academic staff may revisit teaching pedagogies to design new strategies and institutions may develop blended learning guidelines and tools to support academics to embrace blended learning. Social implications The reflection shows the respect, support and care provided by academics to students building a sense of belongingness and supporting students’ mental well-being in a period of fear and anxiety about COVID-19. Originality/value This is a reflection on students’ online engagement during the COVID-19 pandemic, which has not been addressed previously in the academic literature.

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