scholarly journals Unlocking the Potential of Learning Analytics in Computing Education

2017 ◽  
Vol 17 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Shuchi Grover ◽  
Ari Korhonen
Keyword(s):  
Learning Analytics ◽  
Computing Education

IDE-Based Learning Analytics for Computing Education

2017 ◽  
Vol 17 (3) ◽  
pp. 1-26 ◽  
Author(s):  
C. D. Hundhausen ◽  
D. M. Olivares ◽  
A. S. Carter
Keyword(s):  
Learning Analytics ◽  
Computing Education

scholarly journals Multimodal Learning Analytics to Inform Learning Design: Lessons Learned from Computing Education

2020 ◽  
Vol 7 (3) ◽  
pp. 79-97
Author(s):  
Katerina Mangaroska ◽  
Kshitij Sharma ◽  
Dragan Gašević ◽  
Michalis Giannakos
Keyword(s):  
Learning Analytics ◽  
Lessons Learned ◽  
Learning Activity ◽  
Physiological Data ◽  
Learning Design ◽  
Multimodal Learning ◽  
Process Data ◽  
Computing Education ◽  
Log Data ◽  
Multimodal Learning Analytics

Programming is a complex learning activity that involves coordination of cognitive processes and affective states. These aspects are often considered individually in computing education research, demonstrating limited understanding of how and when students learn best. This issue confines researchers to contextualize evidence-driven outcomes when learning behaviour deviates from pedagogical intentions. Multimodal learning analytics (MMLA) captures data essential for measuring constructs (e.g., cognitive load, confusion) that are posited in the learning sciences as important for learning, and cannot effectively be measured solely with the use of programming process data (IDE-log data). Thus, we augmented IDE-log data with physiological data (e.g., gaze data) and participants’ facial expressions, collected during a debugging learning activity. The findings emphasize the need for learning analytics that are consequential for learning, rather than easy and convenient to collect. In that regard, our paper aims to provoke productive reflections and conversations about the potential of MMLA to expand and advance the synergy of learning analytics and learning design among the community of educators from a post-evaluation design-aware process to a permanent monitoring process of adaptation.

Survey and Analysis of Computing Education at Japanese Universities: Non-IT Departments and Courses

2019 ◽  
Vol 13 ◽  
pp. 57-79
Author(s):  
Tetsuro KAKESHITA ◽  
Mika OHTSUKI
Keyword(s):  
Educational Achievement ◽  
Academic Discipline ◽  
Teaching Staff ◽  
Computing Environment ◽  
Computing Education ◽  
Major Subject ◽  
Curriculum Guidelines ◽  
Essential Input ◽  
Organization Quality ◽  
It Departments

We conducted the first national survey of computing education at Japanese universities in 2016. In this paper, we report the survey result of the computing education at non-IT departments and faculties whose major subject is not computing. The survey covers various aspects of computing education including program organization, quality and quantity of educational achievement, students, teaching staff and computing environment. We collected 994 answers through the survey. At least 87,000 non-ICT students are taking computing education in Japan. Although computing education is carried out at every major academic discipline, teaching effort greatly differs depending on the academic discipline. We also find shortage of teaching staff for computing education. The analysis result will be an essential input to develop reasonable curriculum guidelines and accreditation criteria to improve computing education at non-IT departments.

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