Lifelong Learning in higher education using Learning Analytics

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Abstract The conventional education system lacks the focus to create employable graduates. The existing industries in the Information Technology sector widely recruit based on a very specific set of skills and academic performances. To create better career opportunities, the colleges and universities should ensure that the graduates are qualified to accomplish the basic skills required by any organizations. This demands systematic approaches to be adopted with an innovative teaching style during the academic curriculum-based training in engineering colleges. The international accreditation organization ABET is a worldwide recognized educational board that provides streamlined guidelines for competency skills and to deliver a quality education for students. In this research work, an extensive study has been performed on the needs of the industry and it has been compared to the quality of the course being offered to the students. The curriculum is designed based on the industry experts’ feedback. To achieve the Student Outcome Criteria as per ABET accreditation, the practices of structured approach is adopted along with the vision of lifelong learning in this research work. The teaching-learning process of first year under-graduate programming course and its evaluation techniques is considered in this research work. The competency skills like problem solving skills, critical thinking, and creative thinking are analysed using learning analytics strategies for first year Python programming course. The performances of the students are broadly categorized based on the metrics like logical, conceptual, analytical and conceptual thinking, while simultaneously focusing on their time management skills and commitment to learn. Artificial Neural Network (ANN), Naive Bayesian algorithm and logistic regression models are used to identify and measure the competency skills of the learners achieved in this course and validating these metrics with the student learning outcome. Implementing Artificial Intelligence concepts will provide results that can aid in creating the most suitable teaching-learning environment resulting in the best outcome for disruptive engineering education. Learning Analytics will provide an understanding and optimization of learning and its environments thereby ensuring sustainable development. This analysis presents an opportunity to identify the gap between the academic curriculum and the industry’s expectations in terms of competency skills to be acquired by the learners. Additionally, it helps to improve the teaching-learning process according to the dynamic changes from the industries and builds the foundation for students to be lifelong learners.