Exploring Undergraduate Perceptions of Meaning Making and Social Media in their Learning
Notice bibliographique
Résumé
Those concerned with teaching and learning in higher education and the Net generation’s perspectives on and uses of technology must address calls to move beyond the digital native debate (Bennett & Maton, 2010; Kennedy, Judd, Dalgarno, & Waycott, 2010) by asking students directly what they see as a meaningful part of their learning. This study aims to move beyond the digital native debate by developing research-informed understandings of the ways in which Net generation students may perceive technologies, specifically social media, to be a meaningful part of their undergraduate learning. The research questions guiding this study include: (RQ1) In what ways do undergraduate learners from different disciplines view social media to be a meaningful part of their university learning? (RQ2) What characteristics of social media do undergraduate learners see as contributing to their meaning making during their university learning? This study uses a social constructivist approach, thereby employing two main premises: learners actively construct their own knowledge, and social interactions are an important part of knowledge construction (Woolfolk, Winne, Perry, & Shapka, 2010, pp. 343-344). The research design is a mixed methods research (MMR) methodology, a methodological approach where a combination of methods is intentionally used to best address the research questions (Creswell, 2008; Creswell, 2015). This study’s MMR design involved a first phase qualitative component of intensive, semi-structured interviews with 30 undergraduate students enrolled in full-time studies at the University of Alberta, a large, Canadian, research-intensive university – with ten students from each of the three disciplinary areas of 1) humanities and social sciences, 2) health sciences, and 3) natural sciences and engineering, analyzed using a generic qualitative approach (Merriam, 2009) incorporating constructivist grounded theory techniques (Charmaz, 2014). The second phase quantitative component was comprised of undergraduate students across disciplines with survey responses (N = 679) regarding their perspectives on and uses of social media technology in their university learning. This phase included two pilot surveys conducted before the final survey was distributed to ensure the reliability and validity of the instrument developed. Survey responses were collected electronically via SurveyMonkey, and analyzed via descriptive statistics. The findings in this study shed new insights into student perspectives and uses of social media, and the variety of ways in which undergraduates intentionally chose (or, chose not) to incorporate social media into their university learning in meaningful ways. The interviews provide a detailed picture of undergraduate perspectives regarding the specific ways in which social media can help and hinder learning, comprising what students consider as a double-edged sword. Student perspectives and descriptions formed key recurring themes, which emerged into several core characteristics of social media, as well as core categories of meaning making in undergraduate university learning. Within the qualitative interviews and the open-ended survey results, there is an overarching theme of social media as a double-edged sword that both informs and distracts, having the potential to both help and hinder learning. Together, the qualitative and quantitative results demonstrate that several contextual relationships exist, including an important relationship between the particular ways of meaning making identified and the specific social media technologies undergraduates use for their university learning. For those concerned with social media in higher education, these results show how factors such as age and digital native claims should not be seen as primary, deterministic elements of technology use. Rather than taking an approach founded upon technological determinism, the idea of a generational zeitgeist should be considered, where learning context and social media affordances become key.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».