Individual student, home, and school factors in at-risk francophone readers

2008 ◽  
Author(s):  
Marjolaine M. Limbos ◽  
Karli McDonald
Keyword(s):  
At Risk ◽  
Individual Student ◽  
School Factors ◽  
Home And School

Attrition, mental health and student support in engineering education: the Engineering Futures Project

2020 ◽  
Vol 22 (3) ◽  
pp. 8-29
Author(s):  
Jane Andrews ◽  
Robin Clark ◽  
Sukhvinder Phull
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Engineering Education ◽  
Student Attrition ◽  
Attrition Rate ◽  
Individual Student ◽  
Research Approach ◽  
Action Research Approach ◽  
The Individual ◽  
The University

This paper deals with issues surrounding student attrition in engineering education. Looking beyond the traditional markers associated with student attainment, and adopting an action research approach, the “Engineering Futures Project” aimed to tackle an attrition rate in a faculty of engineering that was twice the university average. An algorithm was developed and students ‘at risk’ of not progressing identified. Such students were then contacted individually and offered intensive support and guidance from a member of the project team.Working with academics on a one-to-one basis, students were encouraged to reflect on why they felt they were at risk of not progressing on to the next level of their course. One of the surprising outcomes was that the majority of students indicated they had experienced considerable difficulties with their mental health in the previous 12 months. This, together with a number of other individual issues, impacted their studies. Taking account of the students' perspectives, the project team developed and put in place two distinctive support pathways; one focusing on ‘wellbeing’, the other on “academic support’. Each individual student was given time and assistance to develop their own pathways. Although resource intensive, the Engineering Futures Project was a marked success, drastically reducing attrition and making a notable difference at both the individual and faculty level.

scholarly journals THE EFFECTS OF HUMAN AND MATERIAL RESOURCES ON STUDENTS’ MATH ACHIEVEMENT IN 45 COUNTRIES

2014 ◽  
Vol 62 (1) ◽  
pp. 8-20
Author(s):  
Serdar Aztekin ◽  
Haci Bayram Yilmaz
Keyword(s):  
Human Resources ◽  
International Student ◽  
Linear Models ◽  
Student Assessment ◽  
Hierarchical Linear Model ◽  
Individual Student ◽  
Mathematical Literacy ◽  
School Factors ◽  
Material Resources ◽  
International Student Assessment

This study aims to explore the effects of human and material resources on mathematical literacy. For this purpose, mathematical literacy test scores and questionnaire responses of 304,444 fifteen-year-olds in 45 countries participated in the 2012 cycle of Programme for International Student Assessment (PISA) Project, were analysed through two-level and three-level hierarchical linear models (HLM). Selected indices and scales representing material and human resources’ effects on students’ mathematical literacy were investigated. The results revealed that 23% of the total variance in the literacy scores is attributable to between-countries, 34% of the variance is attributable to between-schools and the remaining 43% to individual student characteristics. Only two school factors, the quality of school educational resources and teacher morale, were found to have effects on students’ performance after accounting for the gender, the index of economic, social and cultural status, and the cumulative expenditure on education. The results of the study have potential to help policy makers determine their priorities in education and provide hints for future studies. Key words: human resources, material resources, PISA 2012, hierarchical linear model.

Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention

2018 ◽  
Vol 57 (3) ◽  
pp. 547-570 ◽  
Author(s):  
Wanli Xing ◽  
Dongping Du
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
At Risk Students ◽  
Individual Student ◽  
Intervention Design ◽  
Temporal Prediction ◽  
Personalized Intervention

Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.

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