Visible to the public Personalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education

TitlePersonalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education
Publication TypeConference Paper
Year of Publication2018
AuthorsDeng, Y., Lu, D., Chung, C., Huang, D., Zeng, Z.
Conference Name2018 IEEE Frontiers in Education Conference (FIE)
Date PublishedOct. 2018
ISBN Number978-1-5386-1174-6
KeywordsArizona State University, cloud computing, cloud-based personalized learning, Computer science, computer science education, computer science hands-on labs, computer security, cyber physical systems, cybersecurity education, data mining, distance learning, educational courses, hands-on lab, learner performance assessment, Learning management systems, Learning Style, online computer science education, personalized lab platform, Personalized Learning, personalized learning platform, privacy, pubcrawl, student performance prediction, ThoTh Lab, undergraduate students, virtual hands-on lab platform, Virtual machining

This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.

Citation Keydeng_personalized_2018