Student-Centered Learning Objects to Support the Self-Regulated Learning of Computer Science

Abstract

The most current computing curriculum guidelines focus on designing learning materials to prepare students for lifelong learning. Under the lifelong learning paradigm, students are responsible for controlling and monitoring their learning processes. This undoubtedly includes the ability to choose suitable learning materials. Correspondingly, instructional paradigms are shifting from teacher-centered to more student-centered models that require students to be self-regulated learners. On the other hand, recent trends in learning materials’ instructional design focus on moving toward the concept of Learning Object-based instructional technology. A learning object is a unit of instruction with a specific pedagogical objective that can be used and reused in different learning contexts. Designing learning objects to support students in their self-regulated learning is not an easy task due to the lack of underlying pedagogical frameworks. It is difficult to find learning objects related to students’ specific preferences and requirements. In this study, a number of learning objects are designed to support the self-regulated learning of programming languages concepts based on the theory of learning styles. Students’ interactions with these learning objects are managed using an online learning object repository. The repository helps students identify their preferred learning styles and find the relevant learning objects. The results of the evaluations of these learning objects revealed that students perceive them to be easy to use and effective in supporting their learning about different programming languages concepts

Using a distributed systems laboratory to facilitate students' cognitive, metacognitive and critical thinking strategy use

“…Despite this, by encouraging students' self-questioning and re-reading, DSLab feedback helped them be aware of SRL and metacognitive strategy use which, in turn, allowed them to achieve more efficient code. This finding is consistent with research which showed that students face difficulties in computer science education when they are not aware of SRL and metacognitive strategy use, so fostering them is important (Alharbi et al, 2012), whereas the use of feedback can be effective in supporting self-monitoring while performing problem-solving tasks (El Saadawi et al, 2010;Kramarski & Gutman, 2006;Lai et al, 2018).…”

“…Though a variety of automated assessment tools has been developed over the years to assess programming assignments (Pettit et al, 2015), there is hardly any study that has investigated how an automated assessment tool for distributed programming could facilitate students' SRL strategies. Some studies in computer science education showed that students face difficulties when they are not aware of SRL and metacognitive strategy use; instead, when they employ SRL and metacognitive strategies they achieve good performance (Alharbi et al, 2012;Alhazbi & Hassan, 2013;Bergin et al, 2005).…”

Being a student in the social media era: exploring educational affordances of an ePortfolio for managing academic performance

“…From above it seems that research should explore the potential of designing effective interventions that encourage the use of SRL strategies through technology-enhanced learning environments (TELEs) (Alharbi et al, 2012). The design and implementation of TELEs (e.g.…”

Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem

“…Unfortunately, there is limited literature on this topic in CS education. Alharbi, Henskens, & Hannaford (2012) reported typical CS students are visual, sensing, reflective, and sequential learners, suggesting that they learn best when exposed to visual representation and facts sequentially. CS students may also prefer to solve problems sequentially and to develop a visual interpretation of the problem.…”