Multi-channel sequence analysis with mixture hidden Markov models¶
Research Method · All research methods
Evidence · 2 claims (2 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
The article offers a detailed methodology for studying how engagement and achievement evolve together across a full program: two chronologically ordered state sequences (engagement and achievement channels) are aligned per course, and a Mixture Hidden Markov Model — "a combination of LCA and HMM" — clusters multichannel trajectories jointly while estimating transition probabilities between hidden states. The method models "several variables evolving in parallel" and reveals for whom and when engagement-achievement relations change across a program.
Accounts¶
- Multi-channel sequence analysis with mixture hidden Markov models for longitudinal engagement-achievement trajectories: The article offers a detailed methodology for studying how engagement and achievement evolve together across a full program: two chronologically ordered state sequences (engagement and achievement channels) are aligned per course, and a Mixture Hidden Markov Model — "a combination of LCA and HMM" — clusters multichannel trajectories jointly while estimating transition probabilities between hidden states. The method models "several variables evolving in parallel" and reveals for whom and when engagement-achievement relations change across a program. (Mohammed Saqr et al. (2023))
Claims¶
- Online engagement at any single time-point is not a consistent indicator of high achievement across a full program, whereas longitudinal patterns of engagement and grades forecast stable academic trajectories [+M]
- Disengagement was consistently and significantly associated with low achievement in all courses of the program [+M]
Related Research Methods¶
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Key Sources¶
- Mohammed Saqr, Sonsoles López-Pernas, Satu Helske, Stefan Hrastinski. (2023). The longitudinal association between engagement and achievement varies by time, students' profiles, and achievement state: A full program study. Computers & Education. https://doi.org/10.1016/j.compedu.2023.104787
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
multi-channel-sequence-analysis-with-mixture-hidden-markov-models |
title |
Multi-channel sequence analysis with mixture hidden Markov models |
description |
The article offers a detailed methodology for studying how engagement and achievement evolve together across a full program: two chronologically ordered state sequences (engagement and achievement channels) are aligned per course, and a Mixture Hidden Markov Model — "a combination of LCA and HMM" — |
status |
review |
generated |
by process:settle-candidates on 2026-10-29 |
trust |
unverified — no one has confirmed this page's content |
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