MétaCan
Menu
Back to cohort
Record W2127565000 · doi:10.1080/09523987.2010.518815

Building a tool to help teachers analyse learners’ interactions in a networked learning environment

2010· article· en· W2127565000 on OpenAlexaff
Ουρανία Πετροπούλου, Ioannis Altanis, Symeon Retalis, Christos A. Nicolaou, Christos Kannas, M. Vasiliadou, Ireneos Pattis

Bibliographic record

VenueEducational Media International · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsInnovaderm (Canada)
Fundersnot available
KeywordsLearning analyticsOnline learningComputer scienceHumanitiesPsychologyPedagogyMultimediaPhilosophyData science

Abstract

fetched live from OpenAlex

Educators participating in networked learning communities have very little support from integrated tools in evaluating students’ learning activities flow and examining learners’ online behaviour. There is a need for non‐intrusive ways to monitor learners’ progress in order better to follow their learning process and appraise the online course effectiveness. This paper presents a conceptual framework and an innovative tool, called LMSAnalytics, that allows teachers and evaluators easily to track the learners’ online behaviour, make judgments about learners’ activity flow and gain a better insight about the knowledge constructed and skills acquired in a networked learning environment. Erstellen eines Tools um Lehrern zu helfen, Lerner‐Interaktionen in einer vernetzten Lernumgebung zu analysieren Pädagogen, die an vernetzten Lerngemeinschaften teilnehmen, haben sehr wenig Unterstützung von integrierten Programmen zum Auswerten der Lernaktivitäten der Studenten und von ihrem Online‐Verhalten. Es ist notwendig, nicht‐störende und auch automatisierte Möglichkeiten zur Überwachung des Lernfortschritts der Lerner zu entwickeln, damit ihr Lernfortschritt und auch die Online‐Kurs‐Effektivität besser verfolgt werden können. Dieses Papier stellt einen konzeptuellen Rahmen und ein innovatives Tool, „LMS‐Analytics”, vor, die Lehrern und Bewertern das Nachverfolgen des Online‐Verhalten des Lernenden und seinen Aktivitätsfluss zu beobachten und dadurch einen besseren Einblick über die Kenntnisse, das Wissen und die Fähigkeiten, die in einer vernetzten Lernumgebung erworben werden, zu gewinnen. La construction d’un instrument pour aider les enseignants à analyser les interactions entre apprenants dans un environnement d’apprentissage en réseau Les éducateurs qui participent aux activités de communautés d’apprentissage en réseau ne sont guère aidés par des instruments intégrés permettant d’évaluer le flux des activités d’apprentissage des étudiants et d’examiner le comportement des apprenants en ligne. Il y a un besoin réel de moyens automatisés et non invasifs pour suivre les progrès des apprenants afin de mieux suivre leur processus d’apprentissage et d’évaluer l’efficacité du cours en ligne. Cet article présente un cadre conceptuel et un outil innovant appelé LMSAnalytics qui permet aux professeurs et aux évaluateurs de suivre facilement à la trace le comportement en ligne des apprenants, de porter des jugements sur le flux d’activité de ces apprenants et d’avoir une vision meilleure du savoir qui s’est construit et des compétences acquises dans un environnement d’apprentissage en réseau. La construcción de una herramienta para ayudar a los profesores a analizar las interacciones entre los estudiantes dentro de un entorno de aprendizaje en red Los educadores que participan en comunidades de aprendizaje en red tienen poca ayuda por falta de herramientas integradas que les permitan evaluar el flujo de las actividades de aprendizaje por parte de los estudiantes y examinar su comportamiento en línea. Lo que hacen falta son sistemas automatizados y non‐invasivos para comprobar los progresos de los estudiantes, vigilar sus procesos de aprendizaje y evaluar la eficacia del curso en línea. Este artículo presenta un marco conceptual y una herramienta innovadora llamada LMS Analytics que ofrecer a los profesores y evaluadores la posibilidad de vigilar fácilmente el comportamiento en línea de los estudiantes, de evaluar sus flujos de actividad y conseguir una visión más clara de los conocimientos construidos y de las competencias adquiridas dentro de un entorno de aprendizaje en red.

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)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0040.007
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.005

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 head0.021
GPT teacher head0.359
Teacher spread0.338 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations12
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEducational Media InternationalSame topicOnline and Blended LearningFrench-language works237,207