Innovative Methodologies for 21st Century Learning, Teaching and Assessment: A Convenience Sampling Investigation into the Use of Social Media Technologies in Higher Education
Bibliographic record
Abstract
The advent of the Web as a social technology has created opportunities for the creation of informal learning environments, which have potential for innovative methodologies in learning, teaching and assessment. However, as Wolfe (2001) admonishes, “contrary to the rhetoric of cheerleaders, the Web places greater demands on students than traditional modes of instruction” (pp. 2 – 3). The pedagogical potential of these high tech, e-skilling, multimedia digital technologies to revolutionize teaching, learning and assessment will only be realized if the underlying theoretical foundations are well articulated and supporting evidence is provided through well-designed empirical research studies. This paper contributes to these two prospects in two ways. First, it articulates the theoretical framework drawn from the work of luminaries in pedagogy that posits cooperative, social learning strategies, as potential methodologies for effective pedagogy. Second, it describes the results of a convenience sampling case study, which investigated the use of cutting-edge social media technologies, namely Google + Discussion Circles, (GDCs), to shed some light on how the use of these social media technologies supported teaching, learning and assessment activities for 2 nd year Bachelor of Education students at a university in Australia. The research found, inter alia, that when students were given the opportunity to learn using GDCs, the majority took advantage of the academic, social and structural dynamics created by these technologies in many ways that supported their learning, assessment activities and overall academic outcomes. The research-based evidence shows that the benefits included high participation rates, great levels of interpersonal interactions among participants, pedagogically rich posts in the GDC streams, metacognitive processing, peer mentoring, ambiguity tolerance, anxiety and motivation. There was also considerable student engagement, exploration of issues, elaboration of what was being discussed in the GDCs, evaluation and explanation, consistent with Bybee et al. (2006) 5E Instructional model for supporting and maximizing students’ learning. The evidence leads to the recommendation that pedagogues at universities and other institutions of higher education should explore opportunities for utilizing selected social media technologies in their pedagogical practices, because, if properly planned and implemented, these technologies appear to have potential so support effective learning, teaching and assessment in the 21 st century. Further research on this topic could also be very beneficial.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".