Active learning analytics in mobile: visions from PhD students. Issue 2 (7th May 2020)

• Record Type: Journal Article
• Authors: Atasoy, Eda; Bozna, Harun; Sönmez, Abdulvahap; Aydın Akkurt, Ayşe; Tuna Büyükköse, Gamze; Fırat, Mehmet
• Is Part Of: AAOU journal. Volume 15:Issue 2(2020)
• Page Start: 145
• Page End: 166
• Publication Date: 2020-05-07
• Subjects: Learning analytics -- Mobile learning -- Open and distance learning -- Communication technologies -- SCAMPER method; Open learning -- Asia -- Periodicals; Distance education -- Asia -- Periodicals; 371.35095
• DOI: 10.1108/AAOUJ-11-2019-0055
• Languages: English
• ISSNs: 2414-6994

Abstract

Abstract : Purpose: This study aims to investigate the futuristic visions of PhD students at Distance Education department of Anadolu University on the use of learning analytics (LA) and mobile technologies together. Design/methodology/approach: This qualitative research study, designed in the single cross-section model, aimed to reveal futuristic visions of PhD students on the use of LA in mobile learning. In this respect, SCAMPER method, which is also known as a focused brainstorming technique, was used to collect data. Findings: The findings of the study revealed that the use of LA in mobile can solve everyday problems ranging from health to education, enable personalized learning for each learner, offer a new type of evaluation and assessment and allow continuous feedback and feedforwards; yet this situation can also arise some ethical concerns since the big data collected can threaten the learners by interfering with their privacy, reaching their subconscious and manipulating them as well as the whole society by wars, mind games, political games, dictation and loss of humanity. Research limitations/implications: The research is limited with the views of six participants. Also, the sample of the study is homogeneous in terms of their backgrounds – their age range, their departments as PhD students and their fields of expertise. Practical implications: The positive perceptions of PhD students provide a ground for the active use of LA in mobile. Further, big data collected through LA can help educators and system makers to identify patterns which will enable tailored education for all. Also, use of LA in mobile learning may stimulate the development of a new education system including a new type of evaluation and assessment and continuous feedback and feedforwards. Originality/value: The widespread use of mobile technologies opens new possibilities for LA in the future. The originality of this research comes from its focus on this critical point.