A Hermeneutic Phenomenological Inquiry into Higher Education Educators' Engagement with Technology for Facilitating Higher Order Thinking Skills
Bibliographic record
Abstract
Adopting a hermeneutic phenomenological approach, this study delves into the lived experiences of higher education (HE) educators who utilise digital technology to enhance Higher Order Thinking Skills (HOTS). Grounded in the philosophical frameworks of Heidegger (1927/1962) and Gadamer (1960/1975) and employing an adaptation of van Manen’s (2014) Methodology of Phenomenology, this research provides an in-depth emic perspective on how educators experience the use of digital tools to foster students’ HOTS. The study engages a diverse, international cohort of twelve HE educators from Australia, Canada, India, the UK, and the USA, covering key disciplines relevant to contemporary business schools.The study aims to enhances understanding of the intricate relationship between digital technology and educators' experiences in fostering HOTS. By exploring these experiences through an existential lens, it addresses ethical, cognitive, and affective dimensions. The findings resonate with insights from Beard (2018) and Hare (2022), affirming technology's dual role in education, as encapsulated in the 'pharmakon' concept, originally articulated by Derrida (1972) from his reading of Plato’s Socrates. This principle illustrates technology's capacity to serve both as a remedy and a poison, a duality further explored in digital contexts by Kern (2014) and Adams (2017). For example, while digital technologies promote connectivity and collaborative learning—serving as a remedy to traditional educational limitations—they can also lead to cognitive overload and superficial engagement, exemplifying its 'pharmakon' effect in supporting HOTS development.By focusing on educators' lived experiences through a phenomenological lens, the research illuminates how educators adeptly navigate the complexities and opportunities presented by digital tools. The findings underscore the necessity for a critical and nuanced understanding of technology's role in shaping educational practices and outcomes and advocates for a balanced, reflective approach to technology integration when supporting HOTS development. The study offers grounded insights to inform pedagogical strategies for HOTS development in the digital era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".