Adult Learning in the Transformational Environments of the Digital Revolution: Connecting Theory and Practice

The purpose of this study was to investigate the learning preferences and the post-secondary educational experiences of a group of Net-Gen adult learners, aged between 18 and 35, currently working in the knowledge economy workplace, and their assessment of how adequately they were prepared to meet the requirements of the knowledge economy workplace. This study utilized an explanatory mixed-method research design. Participants completed a questionnaire providing information on their self-reported learning style preferences, their use of digital tools for formal and informal learning, their use of digital technologies in postsecondary educational experiences, and their use of digital technologies in their workplace. Four volunteers from the questionnaire respondents were selected to participate in interviews based on the diversity of their experiences in higher education, including digital environments, and the diversity of their knowledge economy workplaces. Data collected from the questionnaire were analyzed for descriptive and demographic statistics, and categorized so that common patterns could be identified from information gathered from the online questionnaire and interviews. Findings based on this study indicated that these Net-Gen adult learners were fluent with all types of digital technologies in collaborative environments, expecting their educational experiences to provide a similar experience. Participants clearly expressed an understanding that digital/collaborative aptitudes are essential to successful employment in the knowledge economy workplace. The findings of this study indicated that the majority of participants felt that their post-secondary educational experiences did not adequately prepare them to meet the expectations of this type of working environment.

An Abstract Algebraic Theory of L-Fuzzy Relations for Relational Databases

Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.