The devil is in the detail: A study on quantifying socially shared metacognitive regulation of learning in face-to-face group work
Authors
Kobbelgaard, Frederik Victor ; Kilinska, Daria
Term
4. term
Publication year
2017
Submitted on
2017-05-24
Pages
73
Abstract
I dette speciale undersøger vi socialt delt metakognitiv regulering (SSMR) – hvordan gruppemedlemmer i fællesskab planlægger, overvåger og vurderer deres tænkning og strategier – i studerendes samarbejde. Målet er at finde måder at måle disse processer på, så de kan bruges i læringsanalyse til at støtte bedre samarbejde. Vi analyserede fire timers videooptagelser af fem internationale studerende, der arbejdede med et semesterprojekt under problem‑baseret læring. Med induktive metoder foretog vi først makroniveau‑kodning af et større korpus for at finde relevante øjeblikke og valgte derefter tre klip, der repræsenterede forskellige reguleringsprocesser. To klip blev transskriberet som tekst med fokus på både tale og kropssprog ved hjælp af en tilpasset Jefferson‑notation. En kort planlægningssekvens blev transskriberet grafisk for at indfange de visuelle aspekter af interaktionen. På tværs af abstraktionsniveauer observerede vi: på makroniveau de overordnede SSMR‑processer planlægning, monitorering og evaluering; på mesoniveau mekanismer i planlægning som forhandling af opgavens formål og karakter; og på mikroniveau, hvordan modaliteter som verbal og ikke‑verbal kommunikation, blikretning og gestik omsætter disse mekanismer i praksis. Disse flerniveaubeviser kan bruges til at vurdere kvaliteten af samarbejdet. Vi skitserer også mulige indikatorer for vellykket SSMR baseret på observerede interaktionsmodaliteter. Vores hovedbidrag er refleksioner over, hvordan multimodal læringsanalyse – med data fra enkeltmodaliteter og kombinerede datasæt – kan anvendes til at måle aspekter af SSMR, herunder muligheder og udfordringer.
This thesis examines socially shared metacognitive regulation (SSMR)—the ways group members jointly plan, monitor, and evaluate their thinking and strategies—during collaborative student work. Our goal is to find practical ways to measure these processes so they can be used in learning analytics to support better collaboration. We analyzed four hours of video of five international students working on a problem‑based learning semester project. Using inductive methods, we first applied macro‑level coding to a large corpus to locate relevant moments, then selected three clips that represented different regulatory processes. Two clips were transcribed as text with attention to both speech and body language, using an adapted form of the Jefferson notation. A short planning episode was transcribed graphically to capture visual aspects of interaction. Looking across different levels of abstraction, we observed: at the macro level, group planning, monitoring, and evaluation as overarching SSMR processes; at the meso level, mechanisms within planning such as negotiating the purpose and nature of tasks; and at the micro level, how modalities like spoken and unspoken language, eye gaze, and gestures enact these mechanisms. These multi‑level insights show how evidence of metacognitive regulation can be used to assess the quality of collaboration. We also outline potential indicators of successful SSMR grounded in the observed interaction modalities. Our main contribution is a reflection on how multimodal learning analytics—drawing on data from single modalities and from combined datasets—could be applied to measure aspects of SSMR, including the opportunities and challenges of each approach.
[This abstract was generated with the help of AI]
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