Understanding Teachers’ Information Needs, Perceived Competencies, and Information Seeking Behaviours for Special Education Information
Bibliographic record
Abstract
The focus of this research is to better understand teachers’ information needs, perceived competencies, preferences for information sources, and information seeking behaviours related to special education by level of teaching experience. A mixed methods approach to research was employed using a combination of quantitative and qualitative methods. Eighty-five elementary and intermediate school teachers (J.K. through Grade 8) from Catholic and public school boards in southern Ontario, Canada, completed an online survey questionnaire. Semi-structured, follow-up interviews were then conducted with 11 teachers to further explore the issues. The participants were classified into three experience level groups (i.e., novice, intermediate, and expert teachers) based on 9 indicators of teaching and special education experience and expertise. The participants’ special education information needs were coded to the Council for Exceptional Children’s (CEC) 10 Professional Standards for Special Education (2009) to better understand how teachers with different levels of experience perceive their special education needs and to examine how their needs relate to the CEC’s Professional Standards. The semi-structured interview data was used to provide further illumination on the results of the survey data. Overall, teachers’ most frequently identified needs involved instructional strategies (including differentiated instruction) and how to create inclusive classrooms. More experienced teachers were better able to identify and articulate their special education needs. Distinct patterns of source preferences were found based on teachers' experience levels. Novice teachers most preferred face-to-face consultations with knowledgeable experts and least preferred sources of information that were passive, individual activities such as searching online; reading professional books, magazines, and research resources; or watching videos. They reported being less successful at finding the specific information they needed from these sources. Expert teachers favoured research and professional literature and online sources. Teachers also indicated a preference for one source of online special education information and reported only using a few websites as their main point of access for special education information: school board websites, the Ontario Ministry of Education website, and a variety of disability association websites. The ultimate goal of this research is to provide information on how to better support and meet teachers’ information needs related to special education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.022 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".