%0 Conference Paper
%A Omar, M.K.
%A Sharef, N.M.
%A Murad, M.A.A.
%A Mansor, E.I.
%A Nasharuddin, N.A.
%A Samian, N.
%A Nawi, N.R.C.
%A Arshad, N.I.
%A Ismail, W.
%A Shahbodin, F.
%A Marhaban, M.H.
%D 2021
%F scholars:14244
%I Asia-Pacific Society for Computers in Education
%K Big data; Correlation methods; Machine learning; Motivation; Population statistics; Predictive analytics; Surveys, Analytics systems; Assistive technology; Learning analytic; Learning motivation; Student expectations; Student learning; Student performance; Student' profiling; Teaching and learning; Teaching process, Students
%P 724-726
%T Students' Expectations Toward Features of Learning Analytics System
%U https://khub.utp.edu.my/scholars/14244/
%V 2
%X Learning analytics depicts the process of assessing, evaluating, and measuring student performance and the effectiveness of the teaching and learning process delivered by educators. The objective of learning analytics is to optimize students' learning by maximizing the pedagogical technique, assistive technologies, and cognitive abilities of learners. It is argued that the learning can be varied and subjective, however, with the use of technology, big data application, and machine learning, the process of learning can be empowered through learning analytics. This whole ecosystem proves to be the best practice in understanding student learning needs in tangible ways. The researcher employed descriptive and correlational studies to determine the relationship between learning analytics features and studied variables. Three dimensions of learning analytics were involved in the study: summative, real-time, and predictive. A set of questionnaires was distributed to 350 students enrolled in various programs at Universiti Putra Malaysia. Based on the results, it was found that demographic profiles of the respondents include age, gender, type of student, credit hours intake, concern on achievement, learning preference, and learning motivation contributed significantly to learning analytic features when ANOVA and T-Test being employed in the analytical procedures. Our finding also revealed that there was a strong and positive direction of learning analytic features based on the Pearson Correlation report. In summary, the current study unveils the influence of demographic characteristics of learners on learning analytic features. It is apparent from the findings that the learning analytics features shall consider the extrinsic and intrinsic values of the learners that include assistive technology, learning performance, and motivation. With a blend of values in understanding learning analytics study, the extensive study related to learners profiling is necessary to empower the learning experience comprehensively. Copyright 2021 Asia-Pacific Society for Computers in Education. All rights reserved.
%Z cited By 0; Conference of 29th International Conference on Computers in Education Conference, ICCE 2021 ; Conference Date: 22 November 2021 Through 26 November 2021; Conference Code:175704