Assessing Higher Levels of Learning in Post-Secondary Education. (Online Instruction)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. …
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it