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pith:SWD46MLN

pith:2026:SWD46MLNIKJFFMHDX32FXTD37U

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Cross-modal Affinity-aligned Multimodal Learning Analytics for Predicting Student Collaboration Satisfaction in Game-Based Learning

Chia-Ming Lee, Wen-Hsin Tsai, Yuk-Ying Tung

A module using affinity matrices and contrastive learning to align and selectively suppress unreliable data sources improves predictions of student collaboration satisfaction in game-based learning.

arxiv:2605.16806 v1 · 2026-05-16 · cs.LG · cs.AI · cs.CV

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Claims

C1strongest claim

The CAMA module explicitly models inter-modal relationships via affinity matrices and enforces cross-modal consistency through contrastive learning, enabling adaptive suppression of uninformative modalities without discarding them and yielding consistent improvements over unimodal baselines and prior cross-attention approaches.

C2weakest assumption

That affinity matrices derived from the heterogeneous features (facial action units, head pose, eye gaze, interaction logs) plus contrastive learning will reliably capture and mitigate modality degradation in the specific 50-student EcoJourneys dataset without introducing artifacts that affect downstream satisfaction prediction.

C3one line summary

AAMLA aligns multimodal student data using affinity matrices and contrastive learning to predict collaboration satisfaction in game-based learning despite modality degradation.

References

52 extracted · 52 resolved · 0 Pith anchors

[1] Halim Acosta, Seung Lee, Bradford Mott, Haesol Bae, Krista Glazewski, Cindy Hmelo-Silver, and James C. Lester. Multimodal learning analytics for predicting student collab- oration satisfaction in coll 2024

[2] Waleed Mugahed Al-Rahmi and Mohd Shahizan Othman. Evaluating student’s satisfaction of using social media through collaborative learning in higher education.Interna- tional Journal of Advances in Engi 2013

[3] Comparing collabo- rative and cooperative gameplay for academic and gaming achievements.Journal of Educational Computing Research, 57(8):2110–2140, 2020 2020

[4] Openface 2.0: Facial behavior analysis toolkit

[5] Classifying confusion: Autodetec- tion of communicative misunderstandings using facial action units 2019

Receipt and verification

First computed	2026-05-20T00:03:23.194458Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9587cf316d429252b0e3bef45bcc7bfd01971e56a569474d196725599cba2b9f

Aliases

arxiv: 2605.16806 · arxiv_version: 2605.16806v1 · doi: 10.48550/arxiv.2605.16806 · pith_short_12: SWD46MLNIKJF · pith_short_16: SWD46MLNIKJFFMHD · pith_short_8: SWD46MLN

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SWD46MLNIKJFFMHDX32FXTD37U \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9587cf316d429252b0e3bef45bcc7bfd01971e56a569474d196725599cba2b9f

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2026-05-16T04:22:47Z",
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