This chapter offers a case study in adaptive personalized learning for higher education learners. The chapter presents a postgraduate recommender system for educational pathway to aid with online support towards selecting suitable transferable skills depending on department and captures a current snapshot of the current trends that the university is facing.
Currently, most of the recommender systems that are in a prototype or deployed stage are primarily accuracy oriented. This chapter focuses on teacher preferences for designing serendipity-oriented recommender systems for academic activities. Reports on relevant literature about serendipitous recommenders and fac ulty empowerment with such tools, a focus group study of teachers for some industrial recommender system platforms, and a use case on instructor use of recommenders to inform and support recommendations for lectures are covered. Further, a survey of students to explore the feasibility of student-teacher serendipitous activities and operations are also reported. The results from all three studies show that serendipity has a major role to play in the future. The author surveyed the literature on standard digital libraries and used questionnaire-based data collection and standard statistical methods to evaluate the responses.
— Currently, higher education must open their mindset to change the learning process. This process cannot still stay on a conventional process that only involved students and lecturers as learning participants because to capture what the current industry needs and the trend of knowledge, higher education institutions must collaborate with external parties as learning partners to give a global perspective about the industrial needs and trends. The process to identify a learning partner to contribute to the learning process is not easy way. The higher education institution must select which partner has appropriate skills and competency suitable for the subject’s course. Many parameters will be involved in the selection process to identify the right partner. In this proposed system, external parties in this context consist of professionals or educators from the external institution easily can be defined to be selected as learning partners based on their competency and experience, that listed on social media LinkedIn as a professional platform. The method to build this interactive recommender system in this study is based on design science research that identifies step by step design stage to propose the best way solution for the recommender system. The result of this study is an interactive recommender system that can help higher education to find out the best candidate for their learning partner so the collaboration of learning can be implemented effectively
Interactive Recommender System for Identifying Learning Partner
