#Hashtags: Towards Understanding The Student Experience In Online Discussion-based Learning Environments
Bibliographic record
Abstract
This is an interpretive study focusing on participation in an online graduate-level teacher-education course in a Canadian University. We examined discussion tools within a constructivist framework and reviewed the literature and our own reflective practice around learning management system (LMS) tool use of hashtags. A hashtag is one word or a group of words that begins with the # or pound sign and has no spaces between the words, creating searchable links. Whenever a hashtag is added to a post on PeppeR, our LMS, it is indexed and becomes searchable/discoverable. This study uses an online platform, PeppeR, which was developed at the University attended by all three researchers.. PeppeR was created within the University to support a discussion-based community for collaboration in this and similar higher-education courses within a socially constructed learning environment. This study is meaningful to the microcosm of this Curriculum, Teaching and Learning (CTL) course and also to the wider macrocosm of educators and asks: How do students experience the use of hashtags in asynchronous text-based online discussions? Ostensibly, an LMS allows students to engage in creating and even co-creating knowledge relevant to themselves and their community. Yet, the reality is that students may sometimes find themselves overwhelmed by the mechanics of participating meaningfully in an online academic conversation. Participating online in addressing challenging topics and deciding how to add one’s own voice may seem too ‘loud’, with too many competing voices. Common themes that emerged included finding ways to use hashtags to organize course materials and additional information for follow up later by participants. Also, hashtags associated with discussion posts become learner-driven information and knowledge repositories. This allows for additional forms of participation and user-content creation, including adding potential for meaningful discourse and increased engagement in the learning community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".