Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
3 results
Search Results
Item Restricted Storytelling with Virtual and Augmented Reality in Education Sector(La trobe University, 2024-05-31) Alqahtani, Ruba Hussain; Skarbez, RichardABSTRACT (231 words) Telling stories has been the original teaching tool and a way for children to develop their literacy skills, grammar understanding, and expand vocabulary. Digital storytelling comprises sensory, visual, and aural elements for enhanced learning and teaching process using technological tools and user-designer/user- tool interaction for powerful visual and audio impacts. Immersive storytelling using augmented and virtual reality (AR/VR) could be used in primary education to engage students through interactive storytelling. Immersive storytelling would enhance interest of primary students in learning new concepts which otherwise would be hard to explain to students. The issue is that students are more and more involved in the digital world and bored with traditional teaching experiences. But most people don’t understand the relevance of new technology or understand how to implement it. There are various barriers to the implementation of AR/VR in the education sector and it requires a lot of funds for equipment and training which is not present with most institutes nor do they understand the relevance of investing in such technologies. The purpose of this thesis is to determine ways in which AR/VR could be used to enhance interactive experience in primary education. Through the adopted bibliometric analysis, the thematic evolution and science mapping of AR/VR in education would be highlighted through two comparative timelines (2010-2018) and (2019-current). This research will benefit as it would enhance the teaching capability and knowledge of the students.22 0Item Restricted The Dance Of Order And Chaos: Tracking Keywords Evolution in a Community Over Time(Saudi Digital Library, 2023) Alhazmi, Arwa; Andreas, Gutmann; Ismini, Psychoula; Murdoch, StevenOnline platforms face a persistent challenge in managing prohibited content. As they act to curtail undesired content by blocking search results for specific search terms (keywords) used by malicious actors, they inadvertently impact their associated benign content. Simultaneously, malicious actors cleverly adapt by introducing intentional language variations to those terms. This risks blocking further innocent content and creates openings for undesired content to remain undetected. Therefore, while bad actors’ use of specialized language offers opportunities for content management, it also raises the need for systems adept at detecting the specific terms used across different timeframes, to thwart their efforts efficiently. In this research, we utilize a publicly available time-series dataset of online posts (news articles) to track keyword evolution over time. We posit that methods adept at capturing these shifts can enhance analysis and consequently, the precision of search terms blocking. Our methods leveraged diverse NLP techniques. Firstly, to track change in keywords, news articles were categorized using BERTopic, keywords were extracted for each article using KeyBERT, and afterward, keywords were sampled and carefully represented utilizing tf-idf for different periods. Subsequently, periods were clustered using hierarchical agglomerative clustering to identify patterns and trends. Secondly, our method for tracking contextual change in keywords consisted of identifying keywords, identifying different topics keywords’ representative articles belong to, and setting criteria for defining prominent and shifting topics. Our analysis has yielded promising results, demonstrating that the clustering approach we have adopted for tracking change is adept for handling time-series keywords. Its strength lies in discerning evolving patterns and temporal shifts in keywords and providing insights into ideal time frames for such monitoring. Notably, we were able to identify recurring or seasonal trends, shortterm trends, extended trends, and distinctive keywords isolated within a single month. Moreover, our method and criteria for tracking and analyzing keywords’ usage evolution between through different contexts have proven effective, as evidenced by identifying a contextual shift in 16% of the top 1,000 keywords in our dataset.17 0Item Restricted Keyword Kaleidoscope: Identifying the difference in keywords predominantly used within one community via contrasting with another community(Saudi Digital Library, 2023) Alhazmi, Alaa; Gutmann, Andreas; Murdoch, Steven; Psychoula, IsminiOnline platforms seek to combat unwanted activities and content by implementing measures to block search terms associated with specific keywords frequently used by malicious actors. However, a persistent challenge arises as this approach may inadvertently affect legitimate content that shares these keywords. This study aims to utilize publicly available datasets of online posts to identify differences in the most prominent keywords in these datasets. The goal is to obtain such distinctions by applying similar methods in harmful and benign communities that share similar language and, consequently, employ them toward more effective search term-blocking. To this end, we employed several analysis methods. Keyword frequencies were computed and compared tabularly, visually, and through hypothesis tests. Topic modeling was applied to the reviews from the datasets to examine the keywords within similar topics and their frequencies. Keyword co-occurrences, delineated by how frequently keywords appeared in the same review as each other, were also tallied, and keywords with the top co-occurrence differences were further explored through plots and representative reviews. While this study centered on two reviewer communities, we have discovered several overarching insights, specifically a similar process could be implemented to guide and aid the process of effective banning in search functionalities. The two datasets examined were found to be speaking about similar concepts. While the ordering of the top keywords shifts between the two, the majority of the most frequent keywords are found near the top of both lists. Despite these similarities, however, differences in the overall frequencies of overlapping keywords existed. Notable dissimilarities between the two communities were discovered either as keywords missing from one top list or the other, or in frequency through Pearson’s chi-squared contingency test. The topic model results showed that some topics were present in both communities but were linked to different keywords in each. Finally, the keyword-keyword co-occurrence analysis in this work indicates that even keywords used commonly by both communities can have alternate associations.13 0