Human-Centred Learning Analytics (HCLA) has emerged in the last 5 years as an active sub-topic within Learning Analytics, drawing primarily on the theories and methods of Human-Computer Interaction (HCI). HCLA researchers and practitioners are adopting and adapting HCI theories/methods to meet the challenge of meaningfully engaging educational stakeholders in the LA design process, evaluating systems in use and researching the sociotechnical factors influencing LA successes and failures. This editorial introduces the contributions of the papers in this special section, reflects more broadly on the field's emergence over the last five years, considers known gaps and indicates new opportunities that may open in the next five years.
Accurate assessment and effective feedback are crucial for cultivating learners' abilities of collaborative problem-solving and critical thinking in online inquiry-based discussions. Based on quantitative content analysis (QCA), there has been a methodological evolvement from descriptive statistics to sequential mining and to network analysis for mining coded discourse data. Epistemic network analysis (ENA) has recently gained increasing recognition for modelling and visualizing the temporal characteristics of online discussions. However, due to methodological restraints, some valuable information regarding online discussion dynamics remains unexplained, including the directionality of connections between theoretical indicators and the trajectory of thinking development. Guided by the community of inquiry (CoI) model, this study extended generic ENA by incorporating directional connections and stanza-based trajectory tracking. By examining the proposed extensions with discussion data of an online learning course, this study first verified that the extensions are comparable with QCA, indicating acceptable assessment validity. Then, the directional ENA revealed that two-way connections between CoI indicators could vary over time and across groups, reflecting different discussion strategies. Furthermore, trajectory tracking effectively detected and visualized the fine-grained progression of thinking. At the end, we summarize several research and practical implications of the ENA extensions for assessing the learning process.
Many technology acceptance models used in education were originally designed for general technologies and later adopted by education researchers. This study extends Davis' technology acceptance model to specifically evaluate educational technologies in higher education, focusing on virtual classrooms. Prior research informed the construction of the model, which contains perceived usefulness, perceived ease of use, behavioural intent, access and convenience, system attributes and self-efficacy. Education-specific constructs include cognitive engagement, feedback, instructor practice and class interaction and communication. Additionally, a new construct called comfort and well-being is introduced. A total of 427 valid responses on a 5-point Likert scale were received from university students. Exploratory factor analysis, confirmatory factor analysis and structural equation modelling were used to analyse the data. The model accounted for 78% of variance of behavioural intent, with comfort and well-being demonstrating the strongest influence. Cognitive engagement, access and convenience influenced perceived usefulness, and system attributes and self-efficacy influenced perceived ease of use. Feedback, instructor practice and class interaction and communication were not significant as educational constructs for this cohort. Based on this analysis, a final extended educational technology acceptance model (EETAM) is proposed for further use and testing.
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