High School Students Struggle to Find School-related Information on the Web
Bibliographic record
Abstract
A Review of: Shenton, Andrew K. “The Information-Seeking Problems of English High Schoolers Responding to Academic Information Need.” Library Review 57.4 (2008): 276-88. Objective – To investigate the information-seeking behaviour of high school students looking to meet school-related information needs. Design – Online questionnaire. Setting – A comprehensive, publically-funded high school in north-east England. Subjects – Seventy-seven high school students between the ages of 13 and 18 who responded to an online questionnaire that was distributed to the 900-1000 students enrolled at the institution. Methods – An invitation to participate in an online questionnaire was sent to all students at the high school in October, 2006, via e-mail. The total number of invitations sent was not indicated, although it is noted that current enrolment at the school is approximately 900-1000 students across years 9 to 13. In the e-mail, students were provided with a link to a questionnaire posted on the school’s intranet. The questionnaire consisted of six multiple-choice and three open-ended questions. Qualitative data gathered through an open-ended question about problems encountered when seeking information for school was manually coded, and forms the focus of this article. Main Results – Seventy-seven online questionnaires were completed by students between 31 October and 27 November 2006, when analysis of the data began. Of the 77 respondents, only 35 provided data on problems encountered when seeking information for their assignments. Most of the respondents in this group were in years nine, ten and eleven (ages 13-16), with only two in year 12 (16-17) and four in year 13 (17-18). Over half (19/35) of respondents were female. Forty remaining respondents either stated that they experienced no problems in finding the information they needed for school or did not answer the relevant question on the questionnaire. Two participants indicated that they did not have the information they needed to complete their schoolwork because they did not look for it. Over 20 distinct information-seeking problems were identified through inductive analysis of the qualitative data provided by 35 participants. Difficulties encountered in the search for information largely fell into four major categories: problems determining an appropriate search strategy; barriers posed by limited school resources or Internet filtering software; “process frustrations” (280) stemming from the perceived inadequacies of search engines, poorly designed Web sites, and missing or broken Web links; and, “shortcomings in the retrieved information” (281) in terms of relevance and accuracy. In addition, a small number of students either indicated that they had difficulty applying the information they found to the problem that prompted the search, or were concerned about copyright restrictions on how they could use the information. All but two of the problems reported by students related to information-seeking on the Web. The Web was the most popular source of information for students, with 71 out of 77 respondents listing it as one of the sources or the only source they consulted for school. Conclusion – The results suggest a need for information literacy instruction among high school students, with a particular focus on effective use of the Web. The author suggests that some of the students’ frustrations may have been due to an “over-reliance” on Web resources, and could have been avoided if they were educated in the use of additional types of tools (286). This reliance on Web search engines proved problematic when Web filters impeded the students’ academic research. Some of the problems reported by students in 2006 in the search for academic information were similar to those recounted by students in 1999-2000 for the author’s earlier fieldwork in the same geographic area, including concerns about the accuracy or lack of detail of some Web sources, difficulties identifying effective search terms, and barriers posed by Internet filters. Additional research is needed to determine whether students experience the same difficulties when searching for information to meet personal needs and interests as they do when they are searching for information at the behest of a teacher.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".