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The COVID-19 pandemic has instigated considerable unprecedented challenges to tertiary education in the aftermath of the mass shutdown of universities across Hong Kong since early 2020. Accordingly, shifting the traditional modalities to the online model has been opted for as the only reasonable alternative in the breakout period. It is, then, essential for educators to perceive the extent to which students' learning attainments and experiences have been negatively impacted by these educational changes in the post-pandemic era. With the aim of examining the abrupt changes which the freshmen students' e-learning experiences and their learning environment have undergone before and after the pandemic, the present quantitative study was conducted at a Liberal Arts Institution in Hong Kong, whose data was collected via the web-based questionnaire FYSLES, or "first-year student learning experience survey". Quantitative analysis of responses indicated that students' impressions and experience of liberal arts education, and students' personalities and skills development, presented a U-shaped trend during the COVID-19 pandemic. The underlying rationale of this study is addressed to higher education policymakers to orient and add value to their safety measures, pertinent recommendations, as well as in context strategies that could be established in view of the pandemic's detrimental effects. [ABSTRACT FROM AUTHOR]

E-learning platforms are being used in higher education to liberate teaching and learning from the constraints of time, pace, and space. These platforms provide rich media content, collaborative assessment, and analytics tools and support learning through a variety of online possibilities. However, the focus of most of this research is on learning analytics inside e-learning platforms and does not cover other related institutional surveys and educator professional development activities. This paper outlines a novel conceptual approach for the creation of a teaching and learning data warehouse analytics system that utilizes a Multi-Dimensional Analysis approach to teaching and learning analytics across these e-learning platforms in combination with other institutional data sources, such as various institutional surveys, tracking of professional development activities, and analysis of the use of the learning management system. The novel teaching and learning data warehouse analytics system (TLDWAS) is being developed at Lingnan University, a leading liberal arts university in Hong Kong. The genesis of this project is analyzing large volumes of teaching and learning data and presenting a big picture for senior management to gain insight from massive amounts of student courses and teacher evaluation data, fill the research gaps mentioned above, and help educators identify problems and solutions more effectively. The TLDWAS provides a visual interface for identifying some indicators and storing, classifying, and analyzing teaching and learning data in various forms to generate statistics, analyze models, and identify meaningful patterns in the data in order to improve teaching and learning. The TLDWAS not only provides more evidence-based decision-making for senior management but is also capable of being applied to identify patterns and relations pertaining to students' academic performance, usage of e-learning systems, and associated staff development activities. [ABSTRACT FROM AUTHOR]