Computer-controlled virtual characters are essential parts of most virtual environments and especially computer games. Interaction between these virtual agents and human players has a direct impact on the believability of and immersion in the application. The facial animations of these characters are a key part of these interactions. The player expects the elements of the virtual world to act in a similar manner to the real world. For example, in a board game, if the human player wins, he/she would expect the computer-controlled character to be sad. However, the reactions, more specifically, the facial expressions of virtual characters in most games are not linked with the game events. Instead, they have pre-programmed or random behaviors without any understanding of what is really happening in the game. In this paper, we propose a virtual character facial expression probabilistic decision model that will determine when various facial animations should be played. The model was developed by studying the facial expressions of human players while playing a computer videogame that was also developed as part of this research. The model is represented in the form of trees with 15 extracted game events as roots and 10 associated animations of facial expressions with their corresponding probability of occurrence. Results indicated that only 1 out of 15 game events had a probability of producing an unexpected facial expression. It was found that the “win, lose, tie” game events have more dominant associations with the facial expressions than the rest of game events, followed by “surprise” game events that occurred rarely, and finally, the “damage dealing” events.
The aim of this paper is to demonstrate the degree of teaching staff’s and Students’ satisfaction with the teaching and learning experience via the Blackboard â„¢ Learning Management System (LMS), recently acquired by King Abdulaziz University (KAU) as an online solution supporting distance education programs. Teaching staff’s and students’ perceptions about the e-courses offered via the LMS were collected in four areas: 1) E-course Content, 2) LMS Ease of Use and Performance, 3) Communication Facilitation, and 4) Delivery Methods. The respondents’ observations on these topics were gathered via two short, Likert-scale questionnaires which were distributed during the Summer Semester of 2014. The researchers sought, through this preliminary investigation, to identify issues which might impact the implementation of the Blackboard LMS in the Distance Education programs in KAU. Generally, the results indicate a relatively high satisfaction rating of the LMS from both groups of users.
It is of great importance for Higher Education (HE) institutions to continuously work on detecting at-risk students based on their performance during their academic journey with the purpose of supporting their success and academic advancement. This is where Learning Analytics (LA) representing learners' behaviour inside the Learning Management Systems (LMS), Educational Data Mining (EDM), and Deep Learning (DL) techniques come into play as an academic sustainable pipeline, which can be used to extract meaningful predictions of the learners' future performance based on their online activity. Thus, the aim of this study is to implement a supervised learning approach which utilizes three artifcial neural networks (vRNN, LSTM, and GRU) to develop models that can classify students' final grade as Pass or Fail based on a number of LMS activity indicators; more precisely, detect failed students who are actually the ones susceptible to risk. The three models alongside a baseline MLP classifier have been trained on two datasets (ELIA 101-1, and ELIA 101-2) illustrating the LMS activity and final assessment grade of 3529 students who enrolled in an English Foundation-Year course (ELIA 101) taught at King Abdulaziz University (KAU) during the first and second semesters of 2021. Results indicate that though all of the three DL models performed better than the MLP baseline, the GRU model achieved the highest classification accuracy on both datasets: 93.65% and 98.90%, respectively. As regards predicting at-risk students, all of the three DL models achieved an = 81% Recall values, with notable variation of performance depending on the dataset, the highest being the GRU on the ELIA 101-2.
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