Abstract:

During recent years the field of Learning Analytics have been frequently mentioned in discussions of addressing challenges in education, as well as a means to analyse and draw upon students' strengths in educational contexts. Prognoses for the future labour market show an increasing need of programmers, yet studies show that programming education struggle with student dropout, poor academic performance and low pass rates. The aim of this study was to analyse and discuss potential obstacles and opportunities in using learning analytics tools for forecasting student success in relation to course outcomes in programming education.

This study was carried out as a literature review with a theorical framework for Learning Analytics presented by Yassine, Kadry and Sicilia (2016) in “A framework for learning analytics in moodle for assessing course outcomes. In 2016 IEEE Global Engineering Education Conference (EDUCON) (pp. 261-266). IEEE.” as the basis for a content analysis with deductive coding. Main keywords in the literature search was: learning analytics, programming, education, course, tool, obstacles, opportunities. Keywords was combined with the Boolean operators “and” and “or”. The literature search was limited to recent published research (between years 2015 and 2019).

The study shows that learning analytics tools, if thoughtfully used, is an opportunity to forecast student success and improve educational design, both from the student perspective and from the teacher perspective. Learning analytics tools does not necessarily have to build on quantitative big data analyses only. From a teacher perspective it could be more valuable with a mixed method approach in the strive to improve existing course design. As pointed out in several research studies students’ and teachers’ integrity have to be respected. Today’s virtual learning environments provide huge amounts of learning data, but as in all other types of research, this should build on informed consent. Finally, in a new approach of learning analytics the analyses preferably should include some teaching analytics as well, to better improve course design and learning outcomes.

Keywords:

Learning analytics, Programming education, Programming, Obstacles, Opportunities.