scholarly journals Early Prediction for At-Risk Students in an Introductory Programming Course Based on Student Self-Efficacy

Informatica ◽  
2021 ◽  
Vol 45 (6) ◽  
Author(s):  
Mona Jamjoom ◽  
Eatedal Alabdulkreem ◽  
Myriam Hadjouni ◽  
Faten Karim ◽  
Maha Qarh
2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Daniel Rodríguez-Rodríguez ◽  
◽  
Remedios Guzmán ◽  

Introduction: The relationship that socio-familial and non-cognitive variables have on students in regards to their academic performance is a very important element for success in Secondary Education. In this study the influence of non-cognitive variables (academic self-concept, self-efficacy and perceived family affective support) and socio-familial variables (educational level and expectations of each parent) on the academic performance of secondary school students were analysed. Method: Students were grouped according to their accumulated socio-familial risk index (at-risk students, n = 305; not-at-risk students, n = 991). To measure the variables, the scales What do you think of yourself, General Self-Efficacy and Perceived Family Support were used. Socio-family variables were measured with an ad hoc questionnaire, and academic performance with the end-of-course evaluation scores. Results: The receiver operating characteristic curve showed a decrease in students’ academic performance from three or more accumulated risks. Structural Equation Modelling (SEM) was performed for each group. The results showed that for at-risk students, academic performance was mainly determined by two variables: academic self-concept and self-concept; in contrast to the not-at-risk students in which self-efficacy was the one that had the greatest effect on performance. In both groups, the parents’ expectations were the family variable with the highest incidence being performance, although, for the at-risk group, the effect was greater. Conclusions: The relevance of the identification of non-cognitive and socio-familial variables on the academic performance of at-risk students in regards to secondary education due to socio-familial factors is discussed.


2020 ◽  
Vol 10 (13) ◽  
pp. 4566 ◽  
Author(s):  
Jan Skalka ◽  
Martin Drlik

The number of students who decided to study information technology related study programs is continually increasing. Introductory programming courses represent the most crucial milestone in information technology education and often reflect students’ ability to think abstractly and systematically, solve problems, and design their solutions. Even though many students who attend universities have already completed some introductory courses of programming, there is still a large group of students with limited programming skills. This drawback often increases during the first term, and it is often the main reason why students leave study too early. There is a myriad of technologies and tools which can be involved in the programming course to increase students’ chances of mastering programming. The introductory programming courses used in this study has been gradually extended over the four academic years with the automated source code assessment of students’ programming assignments followed by the implementation of a set of suitably designed microlearning units. The final four datasets were analysed to confirm the suitability of automated assessment and microlearning units as predictors of at-risk students and students’ outcomes in the introductory programming courses. The research results proved the significant contribution of automated code assessment in students’ learning outcomes in the elementary topics of learning programming. Simultaneously, it proved a moderate to strong dependence between the students’ activity and achievement in the activities and final students’ outcomes.


Author(s):  
Ashok Kumar Veerasamy ◽  
Daryl D'Souza ◽  
Rolf Lindén ◽  
Mikko-Jussi Laakso

This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process. 


2016 ◽  
Vol 103 ◽  
pp. 1-15 ◽  
Author(s):  
Farshid Marbouti ◽  
Heidi A. Diefes-Dux ◽  
Krishna Madhavan

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