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All Journal International Journal of Evaluation and Research in Education (IJERE) International Journal of Advances in Intelligent Informatics
Aidil Azli Alias
Universiti Malaysia Sarawak
Computer Science & IT Education

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Teaching workload in 21st century higher education learning setting Hamimah Ujir; Shanti Faridah Salleh; Ade Syaheda Wani Marzuki; Hashimatul Fatma Hashim; Aidil Azli Alias
International Journal of Evaluation and Research in Education (IJERE) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.888 KB) | DOI: 10.11591/ijere.v9i1.20419

Abstract

A standard equation on teaching workload calculation in the previous academic setting only includes the contact hours with students through lecture, tutorial, laboratory and in-person consultation (i.e. one-to-one final year project consultation). This paper discusses teaching workload factors according to the current higher-education setting. Devising a teaching workload equation that includes all teaching and learning strategies in the 21st century higher education learning setting is needed. This is indeed a challenging task for the academic administrators to scrutinize every single parameter that accounted for teaching and learning. In this work, we have discussed the parameters which are significant in teaching workload calculation. For instance, the conventional in-person contact with the students, type of delivery, type of assessment, the preparation of materials for flipped classroom as well as MOOC, to name a few. Teaching workload also affects quality teaching and from the academic perception, the higher workload means lower-quality teaching.
Who danced better? ranked tiktok dance video dataset and pairwise action quality assessment method Irwandi Hipiny; Hamimah Ujir; Aidil Azli Alias; Musdi Shanat; Mohamad Khairi Ishak
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.919

Abstract

Video-based action quality assessment (AQA) is a non-trivial task due to the subtle visual differences between data produced by experts and non-experts. Current methods are extended from the action recognition domain where most are based on temporal pattern matching. AQA has additional requirements where order and tempo matter for rating the quality of an action. We present a novel dataset of ranked TikTok dance videos, and a pairwise AQA method for predicting which video of a same-label pair was sourced from the better dancer. Exhaustive pairings of same-label videos were randomly assigned to 100 human annotators, ultimately producing a ranked list per label category. Our method relies on a successful detection of the subject’s 2D pose inside successive query frames where the order and tempo of actions are encoded inside a produced String sequence. The detected 2D pose returns a top-matching Visual word from a Codebook to represent the current frame. Given a same-label pair, we generate a String value of concatenated Visual words for each video. By computing the edit distance score between each String value and the Gold Standard’s (i.e., the top-ranked video(s) for that label category), we declare the video with the lower score as the winner. The pairwise AQA method is implemented using two schemes, i.e., with and without text compression. Although the average precision for both schemes over 12 label categories is low, at 0.45 with text compression and 0.48 without, precision values for several label categories are comparable to past methods’ (median: 0.47, max: 0.66).
Co-Authors Ade Syaheda Wani Marzuki Hamimah Ujir Hashimatul Fatma Hashim Irwandi Hipiny Mohamad Khairi Ishak Musdi Shanat Shanti Faridah Salleh