Learning Analytics in Computer Education Design

Meena Jha1, Sanjay Jha2

1 Central Queensland University, 400 Kent Street, Sydney, NSW 2000, m.jha@cqu.edu.au 

2 Central Queensland University, 400 Kent Street, Sydney, NSW 2000, s.jha@cqu.edu.au

Learning analytics is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study including business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The field of learning analytics has great potential to inform and enhance teaching and learning practices in higher education. The academic community is seeing current educational systems critically and a lot of issues are being discussed and analyzed. Science, technology, engineering, and mathematics (STEM) fields have notoriously low persistence rates. According to 2016 report on Australia’s STEM workforce only 32% are university qualified. Indeed student retention in STEM discipline is a growing problem. The number of students receiving undergraduate STEM degrees will need to increase as ICT jobs in Australia are expected to grow at a rate of 2% a year upto 2022, more than a third faster that the rate of general job growth. One way to address this problem is by leveraging the emerging field of learning analytics, a data-driven approach to designing learning interventions based on continuously-updated data on learning processes and outcomes.

Learning Management System (LMS) collects user data. It collects all log details. Students and teachers interact with each other via online forums, threaded discussions, and videoconferencing, as well as emails and chats services provided by LMS. These data sets can be analysed to provide answers to: What are the most used resources in computing courses? Who are the most active user in computing courses?. Through an iterative, user-centered, design approach, a learning dashboard can be designed specifically for computing courses. The dashboard can address the important questions relating to learning interventions are: (1) When should the intervention be performed? (2) Whom should the intervention be directed at? (3) What is an effective instructional intervention? LMS provide learners with information content and educational resources. It is an effective way for educators to create, deliver, and manage the educational resources, as well as monitor participation and assess performance among learners.  Learning Analytics (LA), through analysing data and extracting information from LMS can help to find answers to questions that are important to decision-making processes for intervention.  The information on student’s behaviour captured by LMS has been very rarely interrogated and adopted beyond basic load and tool usage. As an educator, we can compare patterns of resource usage, action, or learner’s behaviour by looking at interaction data statements over the term of enrolled user in computing courses. Some aspects of learning such as content engagement, learning preferences, and collaboration can be measured by analysing LMS data. The quantity and diversity of data available regarding student’s online learning behaviour, interactions with peers and teaching staff, and access to other institutional ICT systems (Student services, library, Studiosity, etc..) for example, affords an opportunity for integrating automated student learning and intervention support services. All of these data can be analysed alongside data on students’ learning outcomes in order to identify correlations between learning processes and outcomes, and ultimately to better tailor instruction and intervention to students’ needs. According to our study, students appreciate interventions. Interventions encourage them to get started with the course material, help them prepare for assignments and increase their learning experience.

As an educator need to address (1) How to enable efficient and effective use of teaching resources in computing education; (2) How to identify the need to change and redesign of the computing course assessments regime to ensure all learning objectives are met.