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Personalized Web-Based Learning Services

2005· book-chapter· en· W2897166804 on OpenAlexaff
Larbi Esmahi

Bibliographic record

VenueIGI Global eBooks · 2005
Typebook-chapter
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPersonalizationParadigm shiftProcess (computing)Flexibility (engineering)Lifelong learningDomain (mathematical analysis)Computer scienceExperiential learningKnowledge managementActive learning (machine learning)Synchronous learningControl (management)PerceptionMathematics educationPsychologyCooperative learningTeaching methodPedagogyWorld Wide WebArtificial intelligenceManagement

Abstract

fetched live from OpenAlex

Computers have a great potential as support tools for learning; they promise the possibility of affordable, individualized learning environments. In early teaching systems, the goal was to build a clever teacher able to communicate knowledge to the individual learner. Recent and emerging work focuses on the learner exploring, designing, constructing, making sense of, and using adaptive systems as tools. Hence, the new tendency is to give the learner greater responsibility and control over all aspects of the learning process. This need for flexibility, personalization, and control results from a shift in the perception of the learning process. In fact, new trends emerging in the education domain are significantly influencing e-learning (Kay, 2001) in the following ways: • The shift from studying in order to graduate, to studying in order to learn; most e-learners are working and have well-defined personal goals for enhancing their careers. • The shift from student to learner; this shift has resulted in a change in strategy and control so that the learning process is becoming more cooperative than competitive. • The shift from expertise in a domain to teaching beliefs; the classical teaching systems refer to domain and teaching expertise when dealing with the knowledge transfer process, but the new trend is based on the concept of belief. One teacher may have different beliefs from another, and the different actors in the system (students, peers, teachers), may have different beliefs about the domain and teaching methods. • The shift from a four-year program to graduate to lifelong learning; most e-learners have a long-term learning plan related to their career needs. • The shift to conceiving university departments as communities of scholars, but not necessarily in a single location. • The shift to mobile learning; most e-learners are working and have little spare time. Therefore, any computer-based learning must fit into their busy schedules (at work, at home, when traveling), since they require a personal and portable system.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.138
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1380.106

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 head0.034
GPT teacher head0.342
Teacher spread0.308 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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