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    Demographic analysis of adult learners’ autonomous learning in the context of lifelong learning
    (2025-01-01)
    Wang, Li
    ;
    Sitthiworachart, Jirarat
    ;
    Morris, John
    ;
    Qi, Jinfeng
    This mixed-methods study examined how gender, age and professional background influence adult learners’ autonomous learning at a Chinese Open University. A total of 309 learners from six cities completed the Self-Directed Learning Instrument (SDLI), and the quantitative results were analysed via t-tests and ANOVA. Qualitative data from open-ended responses were thematically analysed. The results showed that the adult learners demonstrated high levels of autonomous learning. While gender and professional background showed significant effects, age was not statistically significant, though many participants perceived it as influential due to life experience and maturity. Learners from natural sciences engineering (NE) and humanities art (HA) majors demonstrated higher autonomous learning, shaped by disciplinary learning strategies. Gender differences were interpreted as contextually driven, related to roles and learning flexibility. Key enablers included motivation, self-efficacy, and learning environment, while challenges included online isolation and technological change. These insights are essential for improving autonomous learning and promote educational equity, refine interventions, optimise resource allocation, enhance curricula, inform policy making, and help adult learners adapt to their lifelong learning needs.
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    Energy-Efficient Continual Learning for Autonomous Driving
    (2023-01-01)
    Ng, Qi Ding
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    ;
    Dilokthanakul, Nat
    ;
    Zhang, Jie
    Our work highlighted the primary challenges of Autonomous Driving (AD), namely the Catastrophic Forgetting (CF) of previous knowledge by the AD system upon new scenario encounters. Considering the infeasible model retraining with past data given computational, power, and storage constraints on the embedded device, we proposed an experiment featuring Avalanche Continual Learning (CL) training strategies to investigate which strategies excel in this task and combine the promising ones in the hope for a more balanced and efficient trade-off between performance and energy consumption. Our experiment unprecedentedly validated the candidates against a new benchmark introducing natural distribution change and time correlation between input images. We found that although a synergy of CL strategies yields higher resistance towards CF, the slight accuracy gain is not worth the additional computation when we account for energy consumption, rendering a simple Replay strategy the best solution for the Continual Learning benchmark for Autonomous Driving: Online Continual Classification (CLAD-C). Our proposal delivers a 65.80% improvement over the baseline at our proposed accuracy-power ratio metric.