Comparative Analysis of Assessment Methods Enabled by Learning Management System in Technical Training Institutions in Kenya

Abstract

Learning Management System (LMS) are software applications designed to manage digital learning content, and their adoption in education has been steadily increasing. However, comprehensive research is needed to assess the effectiveness of LMS in enhancing the instructional process in Kenyan Technical Training Institutions (TTIs). Currently, the utilization of LMS in Kenyan TTIs and its impact on instructional efficiency is unclear, necessitating an in-depth investigation. This study aims to examine and compare the effectiveness of different assessment methods facilitated by LMS in Kenyan TTIs, grounded in Social Constructivism Theory to understand how trainees learn. A mixed-method design was employed, involving qualitative and quantitative approaches, with data collected through questionnaires and interview schedules that were piloted for reliability and validity. The target population included 34 counties with approved TTIs in Kenya, comprising 3400 trainers and 132,000 trainees from 154 institutions. Stratified sampling followed by simple random sampling was used to select respondents. Data was analyzed using descriptive and inferential statistics. Trainers expressed mixed perceptions about LMS competency and time sufficiency, although they generally viewed LMS positively in terms of pedagogy engagement and alignment with learning needs. Challenges identified included technical issues and resistance to change. While traditional assessment methods such as quizzes, exams, and assignments were prevalent, there were suggestions for better alignment with curriculum objectives. Connectivity and technical glitches emerged as significant barriers to effective LMS implementation, highlighting the need for infrastructure investment and comprehensive training. The study found that LMS positively impacts learner engagement, learning, and assessment in TTIs. Regression analysis showed substantial predictive power of LMS variables on policy variance (99.8%), although correlation findings indicated limited direct influence on policy formulation. The study recommends exploring LMS features, addressing technical challenges, integrating technology into curricula through policy, and fostering collaboration between educational institutions and policymakers to develop guidelines addressing infrastructure needs.