Assessing Higher Levels of Learning in Post-Secondary Education. (Online Instruction)
Notice bibliographique
Résumé
Abstract This article describes the results of a study that investigated how to assess higher levels of online learning in post-secondary education. The results of a six item open-ended questionnaire indicated that forms of assessment typically used (i.e., paper and pencil exams) has limited application in online learning environments, indicating a need for alternative assessment strategies. Alternative assessment strategies suggested in the results of this study include the use of negotiated contracting, embedded assessment, learning portfolios, presentations, and repertory grids. ********** Background: Why Alternative Assessment? To receive course credit in formal post-secondary learning environments, we must provide our learners with opportunities to demonstrate that they have acquired an understanding of the content presented. Yet most of us know from our own practices that assessing learning is a difficult process, especially higher levels of learning. How best to assess learning has been the topic of papers and studies, with diverse outcomes and recommendations. For example, Reeves (2000) maintains that traditional assessment (commonly called testing) is being challenged in academic circles by those who favor alternative assessment (p. 103). Taylor, Marienau and Fiddler (2000) assert further that many conventional assessment methods, including essays, unseen exams, and laboratory reports, allow students to take a surface or even implicitly encourage and reward such an approach (p. 309). Based on this rationale, Renner (1997) maintains that assessment activities should be integrated into every learning activity, irrespective of intended learning outcomes. The assessment process is typically even more difficult in online learning environments, especially when we want to move beyond reward systems that encourage surface approaches. While more authentic assessment activities tend to be effective - most specifically at determining whether learners can apply their knowledge and skill to a real (or authentic) task - it is often unclear what kinds of assessment strategies effectively achieve this aim, and whether or not they can be facilitated in online learning. The purpose of this study was to extend our understanding about the use of alternative assessment approaches for assessing higher levels of online learning in postsecondary environments. This study was guided by the following question: What assessment activities can effectively assess higher levels of online learning? This study focused on student assessment in online learning environments within postsecondary institutions. Online learning is referred to as the use of asynchronous Internet integrated distributed learning environments (e.g., WebCT, FristClass, Virtual U, Top Class). Within the scope of student assessment, this study was further focused on formative assessment for higher levels of learning and was concerned with investigating higher levels of learning. This kind of learning has been referred to as higher order learning by Fabro and Garrison (1998) and Resnick (1987). Irrespective of whether it is referred to as higher ordered learning or higher levels of learning, the essence is on the construction of new knowledge. Method This study was built upon the results of a prior study (Kanuka, 2001), which identified six elements as evidence that higher levels of learning was occurring in online environments. The six elements included negotiable learning, instructional, performance-based, new and/or multiple perspectives, assumption identification, and an ability to use a variety of learning strategies. A survey was developed based on these elements and sent to a group of selected experts and scholars in the area of online learning from Canada and the United States. Experts and scholars were defined as those who had a PhD, scholarly publications, and experience using the Web to facilitate teaching and learning in post-secondary institutions. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
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 ».