ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. (June 2015)

• Record Type: Journal Article
• Authors: Ruipérez-Valiente, José A.; Muñoz-Merino, Pedro J.; Leony, Derick; Delgado Kloos, Carlos
• Is Part Of: Computers in human behavior. Volume 47(2015)
• Page Start: 139
• Page End: 148
• Publication Date: 2015-06
• Subjects: Learning analytics -- Architectures -- Decision making -- Visualizations -- Data processing; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2014.07.002
• Languages: English
• ISSNs: 0747-5632

Abstract

Highlights: ALAS-KA is a tool that extends the learning analytics features of the Khan Academy platform. ALAS-KA includes new visualizations for the entire class and individual students of more than 20 new learning indicators. ALAS-KA helps teachers to make decision supported by the high level information provided. ALAS-KA enables students to gain awareness of their learning process for self-reflection. ALAS-KA can be used by the course instructors to detect class tendencies and learner models. Abstract: The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises, or earn badges. This platform provides an advanced learning analytics module with useful visualizations. Nevertheless, it can be improved. In this paper, we describe ALAS-KA, which provides an extension of the learning analytics support for the Khan Academy platform. We herein present an overview of the architecture of ALAS-KA. In addition, we report the different types of visualizations and information provided by ALAS-KA, which have not been available previously in the Khan Academy platform. ALAS-KA includes new visualizations for the entire class and also for individual students. Individual visualizations can be used to check on the learning styles of students based on all the indicators available. ALAS-KA visualizations help teachers and students to make decisions in the learning process. The paper presents some guidelines and examples to help teachers make these decisions based on data from undergraduate courses, where ALAS-KA was installed. These courses (physics, chemistry, and mathematics) for freshmen were developed at Universidad Carlos III de Madrid (UC3M) and were taken by more than 300 students.