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Bibeg Limbu

Publications and source records attributed to Bibeg Limbu.

2 recordsLinked to original sources

Haptics in Cognition: Disruptor or Enabler of Memory?

This exploratory pilot study investigates the impact of haptic perception --specifically tactile sensitivity (touch) and kinaesthetic intensity (movement)-- on learning, operationalized as information retention (immediate recall) through handwriting. Participants (N=20) were randomly assigned to one of four experimental groups in a 2x2 factorial design, manipulating touch (via glove use) and movement (via increased writing pressure). Information retention was measured using an immediate recall test, while mental effort (reaction time in a secondary task) and perceived workload (NASA-TLX) were examined as mediating variables. Bayesian binomial regression revealed moderate evidence that increased writing pressure negatively influenced recall (85-88% probability of negative effect), whereas glove use alone demonstrated no clear effect. Bayesian mediation analysis found no strong evidence that mental effort or perceived workload mediated these effects, as all 95% credible intervals included zero, indicating substantial uncertainty. These findings suggest that increased Kinaesthetic demands may slightly impair immediate recall, independent of perceived workload or mental effort. Importantly, the manipulation of touch alone does not appear to influence information retention. The study contributes to understanding the nuanced relationship between embodied interactions and cognitive outcomes, with implications for designing sensor-based multimodal learning environments.

cs.HC

Eliciting User Requirements for AI-Enhanced Learning Environments using a Participatory Approach

This paper explores the needs and expectations of educational stakeholders for AI (Artificial Intelligence)-enhanced learning environments. Data was collected following two-phased participatory workshops. The first workshop outlined stakeholders' profiles in terms of technical and pedagogical characteristics. The qualitative data collected was analysed using deductive thematic analysis with Activity Theory, explicating the user needs. The second workshop articulated expectations related to the integration of AI in education. Inductive thematic analysis of the second workshop led to the elicitation of users' expectations. We cross-examined the needs and expectations, identifying contradictions, to generate user requirements for emerging technologies. The paper provides suggestions for future design initiatives that incorporate AI in learning environments.

cs.HC