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Record W4328100102 · doi:10.18438/eblip30164

Exploring Library Activities, Learning Spaces, and Challenges Encountered Towards the Establishment of a Learning Commons

2023· article· en· W4328100102 on OpenAlexvenueno aff
Maryjul Beneyat-Dulagan, David A. Cabonero

Bibliographic record

VenueEvidence Based Library and Information Practice · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDescriptive statisticsReading (process)Mathematics educationNonprobability samplingProblem statementComputer scienceLibrary scienceMedical educationWorld Wide WebPsychologySociologyMathematicsMedicinePopulationEngineeringPolitical science

Abstract

fetched live from OpenAlex

Objectives – This study was conducted to determine the library activities, preferred learning spaces, and challenges encountered by the students of Mountain Province State Polytechnic College (MPSPC) Library, Philippines. Specifically, it sought to answer the following problems: 1) What are the library activities of MPSPC students?; 2) What are the preferred learning spaces in terms of a) physical environment and b) virtual environment?; and 3) What are the challenges associated with library learning activities encountered by the MPSPC students? The study then will be used to explore the feasibility of proposing a learning commons. Methods – This study used a descriptive research method to determine the library activities, learning spaces, and challenges encountered by MPSPC students in the Philippines. It made use of a researcher-made survey questionnaire. Problem statement number 1 dealt with the library activities of MPSPC students. Problem statement number 2 dealt with the preferred learning spaces. Data were gathered from 500 graduate and undergraduate students from a total of 3,015 enrolled during the first semester of the SY 2019-2020 using a purposive random sampling technique. Descriptive statistics such as frequency, percentage, and rank were used. Results – The most frequent library learning activities performed by the MPSPC students were doing assignments, using reference books, searching/browsing printed materials, reviewing notes, and writing. Students’ least frequent library activities were surfing the web, using the computer, using e-resources, eating while reading/writing, and sleeping. The most preferred physical learning spaces were a makerspace, group study spaces, quiet study rooms, and individual study spaces (individual study carrels), while the most preferred virtual learning spaces were computer workstations, interactive learning spaces, video viewing stations, and internet cafés. The overall challenges encountered by MPSPC students were insufficient learning spaces, poor internet connection, inability to find documents or books needed, lack of reading area, lack of printing or photocopying service, lack of professional books, and lack of e-resources. The least challenges encountered by MPSPC students included very high library fees, poor ventilation, poor lighting facility in the designated area, uncomfortable furniture, and lack of staff’s kindness. Conclusion – The MPSPC students perform various educationally purposeful library activities, which are generally engaging and support the library's mission. Students vary in their needs of physical and virtual learning environments. Both of these learning spaces are in demand among students, which are the key components of the learning commons. Also, they specified the need for adequate learning spaces to support their various library learning activities. The findings serve as the basis for crafting a project proposal to establish a learning commons tailored to MPSPC students’ library activities and preferred learning spaces, with consideration for the challenges encountered by students, to support their learning and academic success.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.084
GPT teacher head0.266
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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