Mining student data to assess the impact of moodle activities and prior knowledge on programming course success

Sisovic, Sabina; Matetić, Maja; Bakarić, Marija Brkić

doi:10.1145/2812428.2812459

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…Yasmine 201338. University of Rijeka Data mining, analysis Find out factors leading to student success in study Identify problems timely and increase the course pass rate Sisovic et al (2015) (continued ) Information extraction, analysis Extract information useful for teaching/administrative staff, such as interaction of students with peers, teachers, the system, and course contents Provide teachers with tools to facilitate managerial tasks Conde et al (2015) Support practical learning in a 3D virtual environment, analyse the problems that arisen, and report relevant data to students and teachers Cruz-Benito et al (2014) 40. University of Santiago de Compostela Analysis, evaluation Generate automatically reports of learners' activities that take place in a virtual learning environment Improve the efficiency of the evaluation process Gewerc et al (2014) 41.…”

Section: Madridmentioning

confidence: 99%

Learning analytics in higher education: an analysis of case studies

Wong

2017

AAOUJ

111

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Purpose-The purpose of this paper is to present a systematic review of the mounting research work on learning analytics. Design/methodology/approach-This study collects and summarizes information on the use of learning analytics. It identifies how learning analytics has been used in the higher education sector, and the expected benefits for higher education institutions. Empirical research and case studies on learning analytics were collected, and the details of the studies were categorized, including their objectives, approaches, and major outcomes. Findings-The results show the benefits of learning analytics, which help institutions to utilize available data effectively in decision making. Learning analytics can facilitate evaluation of the effectiveness of pedagogies and instructional designs for improvement, and help to monitor closely students' learning and persistence, predict students' performance, detect undesirable learning behaviours and emotional states, and identify students at risk, for taking prompt follow-up action and providing proper assistance to students. It can also provide students with insightful data about their learning characteristics and patterns, which can make their learning experiences more personal and engaging, and promote their reflection and improvement. Originality/value-Despite being increasingly adopted in higher education, the existing literature on learning analytics has focussed mainly on conventional face-to-face institutions, and has yet to adequately address the context of open and distance education. The findings of this study enable educational organizations and academics, especially those in open and distance institutions, to keep abreast of this emerging field and have a foundation for further exploration of this area.

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Section: Madridmentioning

confidence: 99%

Learning analytics in higher education: an analysis of case studies

Wong

2017

AAOUJ

111

View full text Add to dashboard Cite

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“…Generally, secondary school graduates are not equally ready for STEM courses due to differences in school types and maths curricula [85]. Students' prior knowledge has been found repeatedly in the selected articles to be related to academic success in STEM subjects [55,56]. Particularly for mathematic proficiency, students with basic mathematics deficiencies have a higher chance of failure or dropping out of STEM-related subjects [86].…”

Section: Student Prior Academic Performancementioning

confidence: 99%

Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review

Herbert

Yeom

et al. 2022

Education Sciences

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Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success and retention. However, the retention factors in learning analytics practice for STEM education have not been fully reviewed and revealed. The purpose of this systematic review is to contribute to this research gap by reviewing the empirical evidence on factors affecting student persistence and retention in STEM disciplines in higher education and how these factors are measured and quantified in learning analytics practice. By analysing 59 key publications, seven factors and associated features contributing to STEM retention using learning analytics were comprehensively categorised and discussed. This study will guide future research to critically evaluate the influence of each factor and evaluate relationships among factors and the feature selection process to enrich STEM retention studies using learning analytics.

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“…Sisovic et al [32] applied educational data mining and learning analytics techniques in order to find out what impacts success in programming. The research was conducted on the dataset compounded of extracted logs from the LMS Moodle and data related to prior knowledge and students' preparation for the study.…”

Section: Related Workmentioning

confidence: 99%

Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students’ Outcomes in the Introductory Programming Courses

Skalka

Drlík

2020

Applied Sciences

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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.

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Mining student data to assess the impact of moodle activities and prior knowledge on programming course success

Cited by 11 publications

References 5 publications

Learning analytics in higher education: an analysis of case studies

Learning analytics in higher education: an analysis of case studies

Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review

Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students’ Outcomes in the Introductory Programming Courses

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