Collaborative information behavior in learning tasks: a study of engineering students
Bibliographic record
Abstract
Collaborative information behaviour is an emerging area in information science that deals with the identification, seeking, searching, and use of information by two or more people to accomplish a task. This dissertation investigates the collaborative information behaviour of senior undergraduate engineering students working on group design-projects at a Canadian university. The dissertation presents a longitudinal research using a constructivist grounded theory methodology in two different but related studies undertaken in successive academic years. The main research method consisted of a web-based survey, bimonthly semi-structured interviews with eight students, and the project deliverables for six different project groups. Project deliverables included weekly reports that described group and project activities, and the projects' interim and final reports. The research results show that learning tasks associated with engineering design projects were information-intensive tasks; information seeking, searching, and use have been ongoing needed activities during the lifespan of these projects. There was found to be a strong relationship among learning task stages and phases, task complexity, and collaborative information behavior. Collaborative information behaviors occurred variably at different project stages and levels, and their nature were task-dependent. Students' perception of task complexity triggered collaborative seeking and use of a variety of information sources, with preferences for information from perceived subject-experts. It was also found, in many situations, when students' perceived task complexity increased, their information behavior tended to be more collaborative.The study highlighted the need for groups to construct and share a collaborative situation awareness in order to maintain and regulate their activities in information seeking and use; this shared awareness was enabled by students' interactions in their group meetings or their use of collaborative software tools for information sharing. Learners sought and created meaning from information through collaborative information synthesis over long intervals by prioritizing, judging relevance, and building connections of information. The research investigated collaborative information behavior in learning tasks through a detailed analysis of findings that resulted in a holistic conceptual framework illustrating the dynamic interplay of the components of task-based collaborative information behavior in learning tasks. Collaborative information behavior was conceptualized with details in its three distinct but interrelated dimensions: (1) learner's knowledge, (2) learners' activities and interactions, and (3) information objects; the representation of interdependence of these three dimensions confirmed the complexity of collaborative information behavior as a human behavior that cannot be investigated by focusing on a single dimension and eliminating the other ones.The dissertation presents original research that extends our conceptual understanding of students' collaborative information behavior in learning tasks and also provides more insights into how collaborative information behaviors are dynamically shaped by the characteristics of the learning task.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".