Bibliographic record
Abstract
In response to significant global events such as the COVID-19 pandemic, educational environments are undergoing a fundamental transformation towards collaborative online spaces and networked learning. Networked learning includes (a) the process of learning with and through other people and resources and (b) the environment (i.e., the internet) and platforms (i.e., YouTube, websites, social media, discussion forums) that support these connections or networks (Hodgson & McConnell, 2019). This shift in how learning is done necessitates a reevaluation of pedagogical methods to foster the development of students' global skills and competencies. These competencies, as defined by the Council of Ministers of Education Canada (CMEC), are recognized as essential for individuals to not only adapt but thrive in our current and future world. This world is characterized by unprecedented simultaneous challenges, often referred to as a 'polycrisis,' and rapid advancements in artificial intelligence (A.I.) that have the potential to reshape every aspect of human existence. Therefore, it is imperative that we delve into a deeper investigation and understanding of innovative pedagogical approaches to ensure students are adequately prepared for the evolving landscape. Collaboration is arguably one of the most important of the global skills and competencies as it underpins many of the essential skills youth need to thrive in educational and non-educational settings. More specifically, collaboration underpins the type of networked learning rising in popularity in formal and informal learning settings (Bülow & Nørgård, 2021). As a result, this exploratory research focuses on Minecraft: Education Edition (M:EE) as a tool for developing collaboration through critical making and team-based learning. Over a five-day spring-break camp, two cohorts of students (grades four to six and grades seven to eight) participated in open-ended design-based learning challenges online (in the virtual meeting platform, Google Meet, and in the virtual world, M:EE). Data analysis revealed that collaboration manifested itself in three primary modes: co-constructing knowledge, peer-teaching, and conflict management. Analysis further revealed that younger versus older students build and collaborate in the online environment very differently, which at times mirrored the 'real world' classroom. These findings have implications for designing age-appropriate online learning experiences to support collaboration in a networked environment, especially within virtual simulation and creation worlds like Minecraft.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.549 | 0.360 |
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".