Exploring the representation of multiple intelligences (MIs) in self-instructional materials (SIM) materials in the learning management system (LMS)
Nagaletchimee Annamalai et al.
What the paper says
Purpose This study aimed to explored the integration of Gardner’s Multiple Intelligences (MI) theory within a learning management system (LMS), focusing on the adaptability of technology-enhanced learning environments in accommodating diverse learning styles. Design/methodology/approach A comprehensive strategy utilising a sequential mixed methods approach was utilised to investigate the research enquiries. The survey was completed by 584 undergraduate students from a university in Malaysia. Subsequently, 20 interviews were examined using Braun and Clarke’s Thematic Analysis (Braun and Clarke, 2006). Findings The investigation reveals that SIM within the LMS aligns with all eight multiple intelligence indicators proposed by Gardner, showcasing a comprehensive integration compared to traditional textbooks. Student experiences related to multiple intelligences within the LMS are positively indicated, emphasising linguistic, visual/spatial, interpersonal, music, naturalistic, kinaesthetic, intrapersonal and mathematical intelligence. Practical implications Pedagogical implications suggest educators customise SIM to accommodate diverse intelligences, incorporating varied assessment methods within the LMS. The study concludes with the importance of adaptability, diverse assessments, practical application and innovative tools to create a dynamic virtual learning environment, urging educators to explore strategies that cater to diverse student intelligences. Originality/value This paper introduces a novel approach by exploring the integration of Howard Gardner’s MI theory within an LMS. The focus on adapting technology-enhanced learning environments to accommodate diverse learning styles is an innovative perspective. While the application of MI theory in education is not new, the study’s emphasis on incorporating it into a digital platform like an LMS represents a unique and forward-thinking approach.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.