This study investigates how the African philosophy of ubuntu can be harnessed to foster critical thinking - particularly good thinking habits - among first-year financial accounting students in an online learning environment. As critical thinking becomes an essential skill for accounting graduates navigating a rapidly evolving profession shaped by digital automation and complexity, developing these competencies in an Open Distance e-Learning (ODeL) context presents unique pedagogical challenges. Drawing on the constructivist research paradigm, the study examines accounting academics’ perceptions of how ubuntu principles - such as respect, care, interconnectedness, and shared responsibility - can complement social constructivist approaches to enable deeper learning and support the development of the affective domain of critical thinking. A qualitative case study design was employed, with semi-structured face-to-face interviews conducted with 10 academics at a South African ODeL institution. Participants were purposively selected based on their expertise in first-year modules, decision-making roles, and insight into exit-level competencies. Data were analysed using a codes-to-themes-to-theory model, supported by ATLAS.ti™ 8. The findings revealed that ubuntu values align closely with constructivist principles such as social interaction, contextualised learning, and the centrality of student agency. When embedded in learning design and assessment, these values help cultivate critical thinking habits such as inquisitiveness, perseverance, honest reflection, adaptability, and an appreciation for diverse perspectives. The study concludes that ubuntu-informed pedagogy has the potential to humanise digital learning spaces, ground education in culturally resonant values, and support the development of empathetic and ethically aware graduates. The findings offer practical implications for educators and curriculum developers seeking to design inclusive and high-impact learning experiences in ODeL contexts. Furthermore, the study contributes to advancing indigenous knowledge systems in education and to the broader global discourse on fostering critical thinking in digitally mediated learning environments.
It is a challenge to promptly respond to the learning needs of students, particularly adult learners characterized by rich lived-experiences, (sometimes) negative prior learning experience, multiple and contrasting motivation types alongside considerable socio-demographic heterogeneity. Furthermore, developing a typology of adults’ learning needs remains a challenging endeavour given the different perspectives in learning needs conceptualization and the different theories concerning adult learning in both traditional and online and blended learning (OBL). In particular, no validated instrument currently exists to identify and measure adult learners' needs in OBL contexts, representing a gap that needed to be addressed. This paper developed and validated an instrument to identify adults’ learning needs based on adult learning, self-regulated and motivational theories, and technological acceptance models. We deconstructed adults’ learning needs into three categories based on the existence, relatedness, and growth (ERG) theory of human needs. Data (N=209) were collected from adult learners following blended learning programs in eight centres of adult education in Flanders, Belgium. Confirmatory factor analysis (CFA) revealed that the instrument comprising of nine dimensions of learning needs displayed adequate model fit and factor loadings. Multivariate analysis of covariance (MANCOVA) highlighted that learners who differed in educational attainment, age, and ICT skills reported differences in their needs across the sub-dimensions. Motivational orientations, however, did not significantly differentiate learning needs at the multivariate level. The validated instrument, hence, offered practitioners a diagnostic tool for needs-based OBL design. Theoretically, the study challenged the assumption that motivational orientation alone predicted differentiated need profiles in OBL, opening new avenues for debate on the interplay between motivation, prior experience, and adult learning needs.
In recent years, higher education has increasingly emphasized the integration of Learning Design and Learning Analytics to foster more engaging, personalized, and effective learning environments. This systematic literature review investigates how these two domains interact to enhance teaching learning processes and improve educational outcomes. The review identifies key benefits and opportunities associated with this integration across three stakeholder groups: students, lecturers, and educational institutions by analyzing 55 peer-reviewed publications. The results show that learning effectiveness can be significantly enhanced through the visualization of students’ learning interactions using straightforward and user-friendly analytical approaches. Furthermore, successful implementation requires the development of lecturers’ data literacy and programming competencies, as well as the incorporation of sociocultural, psychological, and physical data to achieve a more holistic understanding of learners. The review also identifies four major research directions to guide future efforts in bridging Learning Analytics and Learning Design. Finally, the paper underscores the importance of establishing clear ethical and privacy frameworks to ensure the responsible application of Learning Analytics in higher education.
Classifying student engagement accurately is critical for timely academic intervention; however, most existing approaches rely on arbitrarily defined thresholds that lack statistical grounding and are difficult to transfer across institutional contexts. This limitation reduces the practical applicability of engagement analytics in diverse educational settings. This study evaluates a quantile-based engagement classification framework across two contrasting datasets to assess its validity, transferability, and consistency of predictive features. Unlike threshold-based approaches, the proposed framework derives engagement categories directly from dataset-specific interaction distributions. The Open University Learning Analytics Dataset (OULAD) represents large-scale fully online learning, while the Unistudium dataset reflects a smaller blended learning context. The two datasets differ substantially in size and delivery mode, with a student ratio of approximately 17.4 to 1. This contrast provides a rigorous basis for assessing method transferability. Engagement categories (passive, moderate, and active) are derived using dataset-specific quartile thresholds (Q1 and Q3). This strategy adapts automatically to local interaction distributions and avoids manual parameter tuning. Five temporal behavioural features were extracted, including active days, unique actions, and learning consistency. Random Forest was employed as the proposed model, while a Decision Tree classifier was included as a baseline for comparative evaluation. The results indicate that the proposed framework remains effective across different educational contexts. In the OULAD dataset, the model achieved an accuracy of 92.04% with a Cohen's kappa of 0.87. In the Unistudium dataset, accuracy reached 72.50% with a Cohen's kappa of 0.59. Although performance differed between datasets, variance remained low. Feature importance analysis further revealed strong consistency across contexts, with a Spearman correlation of 0.90. Active days and unique actions were the most influential predictors in both cases. The baseline comparison further confirmed the superiority of Random Forest over the Decision Tree baseline across both datasets. These findings support e-learning practice by offering institutions a statistically grounded and automated method for engagement classification. The approach removes the need for arbitrary thresholds and reduces operational overhead in analytics deployment. From a research perspective, the study establishes realistic performance benchmarks for engagement analytics at different institutional scales, demonstrates the applicability of quantile-based engagement classification across heterogeneous datasets, and confirms that key behavioural engagement indicators transfer reliably across online and blended learning environments.
This study explores how early-years teachers evaluate and implement Computational Thinking (CT) through tangible floor-robot activities within an eTwinning Community of Practice (CoP), with attention to gender-related participation patterns. Although CT is increasingly recognised as an essential dimension of Early Childhood Education (ECE - referring to ages 4-6 according to the Greek Education System), there is still limited empirical evidence on how teachers transform CT concepts into developmentally appropriate practice and how gender may shape children’s engagement in such activities. Addressing these gaps, the study focuses on teachers’ evaluations and classroom implementation of CT, their experiences with tangible programming tasks, and their perceptions of gender-related patterns in participation, support needs, and CT performance. The research was conducted over a 24-week asynchronous professional-learning programme hosted on Moodle and involved one national cohort of Greek early-years educators (N = 473). During the professional-learning programme, participants engaged in weekly STEAM-oriented CT activities and collaboratively designed classroom learning scenarios using floor robots (e.g., Bee-Bot). Survey instruments captured teachers’ perceptions of CT, educational robotics, STEAM pedagogy, and gender-related classroom observations, while focus-group discussions provided complementary insights into classroom enactment. Following a DBR-informed exploratory mixed-methods approach, the research combined iterative engagement in the CoP with descriptive analysis of survey and focus-group data to identify patterns in teachers’ experiences and reported classroom practices. Findings indicate consistently high levels of participation for both girls and boys, with only small gender differences. Boys appeared slightly more often in the highest engagement band but were also more likely to require ongoing support, whereas girls were more frequently described as working independently and showed a modest descriptive advantage in problem solving, sequencing, and simple algorithm design. In addition, the learning scenarios were rated as useful or very useful for cultivating CT and for fostering collaboration, communication, and problem solving in early-years classrooms. Qualitative findings identified three design features as particularly effective in supporting children’s engagement: explicit sequencing supports, structured testing and debugging cycles, and the use of cooperative roles. Taken together, these findings underpin a set of practical learning-design principles for implementing CT through floor-robot activities in early childhood. More broadly, the research illustrates how an online CoP can support the adaptation of CT-focused designs into everyday classroom practice. It also contributes to e-learning research by illustrating how developmentally appropriate robotics activities within a Community of Practice may support equitable opportunities for young children to engage with foundational CT practices from the earliest years of schooling.
