Recommending Items in Social Tagging Systems Using Tag and Time Information

In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this candidate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation coming from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on data-sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure

The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study

In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations. Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a context-aware tag recommendation algorithm called 3Layers with the user acceptance of a simple popularity-based baseline. In this scenario, we validated and verified the hypothesis that semantic context cues have a higher impact on the user acceptance of tag recommendations in a collaborative tagging setting than in an individual tagging setting. With this paper, we contribute to the sparse line of research presenting online recommendation studies.Comment: 2 pages, poste

Exploring leaders' perceptions of school resilience during COVID-19: Constructing the framework for school development

The study aimed to develop a framework for evaluating school resilience, focusing on leaders' perceptions. This framework examined two critical aspects: firstly, how pre-pandemic school development activities influenced schools' response during the COVID-19 remote learning period, and secondly, how experiences gained during the pandemic could shape future practices.Two research questions guided the study: (1) What predictive factors contribute to leaders’ ability to manage daily leadership during distance learning? (2) What factors do leaders identify as enabling schools to enhance their learning capacity for the future?The survey was conducted among Estonian school leaders at the end of the first pandemic wave in June 2020. Two multiple linear regression models were employed to explore the variance in coping with leadership challenges during the remote learning period and the potential impact on future teaching practices.The results show that leaders effectively managed the challenges of the remote learning period, drawing on their pre-pandemic experience with joint discussions and distance teaching practices, established routines working with data and collaboration with parents. Moreover, the perception of the distance learning period as a learning opportunity for the future stemmed from their previous experiences in distance teaching, data-driven decision-making, and joint discussions with colleagues.The developed resilience framework serves as both a research and self-evaluation tool to assess schools' capacity to navigate uncertainty. Additionally, it can be integrated into leadership training or school development programs to bolster schools' resilience and cultivate context-sensitive leadership skills