Longitudinal Cognitive Diagnostic Assessment Based on the HMM/ANN Model Article Swipe
Hongbo Wen
,
Yaping Liu
,
Ningning Zhao
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2020.02145
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2020.02145
Cognitive diagnostic assessment (CDA) is able to obtain information regarding the student’s cognitive and knowledge development based on the psychometric model. Notably, most of previous studies use traditional cognitive diagnosis models (CDMs). This study aims to compare the traditional CDM and the longitudinal CDM, namely, the hidden Markov model (HMM)/artificial neural network (ANN) model. In this model, the ANN was applied as the measurement model of the HMM to realize the longitudinal tracking of students’ cognitive skills. This study also incorporates simulation as well as empirical studies. The results illustrate that the HMM/ANN model obtains high classification accuracy and a correct conversion rate when the number of attributes is small. The combination of ANN and HMM assists in effectively tracking the development of students’ cognitive skills in real educational situations. Moreover, the classification accuracy of the HMM/ANN model is affected by the quality of items, the number of items as well as by the number of attributes examined, but not by the sample size. The classification result and the correct transition probability of the HMM/ANN model were improved by increasing the item quality and the number of items along with decreasing the number of attributes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyg.2020.02145
- https://www.frontiersin.org/articles/10.3389/fpsyg.2020.02145/pdf
- OA Status
- gold
- Cited By
- 14
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3084370732
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3084370732Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fpsyg.2020.02145Digital Object Identifier
- Title
-
Longitudinal Cognitive Diagnostic Assessment Based on the HMM/ANN ModelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-09Full publication date if available
- Authors
-
Hongbo Wen, Yaping Liu, Ningning ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyg.2020.02145Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fpsyg.2020.02145/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fpsyg.2020.02145/pdfDirect OA link when available
- Concepts
-
Hidden Markov model, Cognition, Artificial intelligence, Artificial neural network, Computer science, Machine learning, Psychology, Pattern recognition (psychology), NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 5, 2023: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.model | 48, 64, 93, 137, 175 |
| abstract_inverted_index.size. | 163 |
| abstract_inverted_index.study | 33, 78 |
| abstract_inverted_index.Markov | 47 |
| abstract_inverted_index.hidden | 46 |
| abstract_inverted_index.items, | 144 |
| abstract_inverted_index.model, | 56 |
| abstract_inverted_index.model. | 20, 53 |
| abstract_inverted_index.models | 30 |
| abstract_inverted_index.neural | 50 |
| abstract_inverted_index.number | 105, 146, 154, 185, 192 |
| abstract_inverted_index.obtain | 7 |
| abstract_inverted_index.result | 166 |
| abstract_inverted_index.sample | 162 |
| abstract_inverted_index.skills | 125 |
| abstract_inverted_index.small. | 109 |
| abstract_inverted_index.(CDMs). | 31 |
| abstract_inverted_index.HMM/ANN | 92, 136, 174 |
| abstract_inverted_index.applied | 60 |
| abstract_inverted_index.assists | 116 |
| abstract_inverted_index.compare | 36 |
| abstract_inverted_index.correct | 100, 169 |
| abstract_inverted_index.namely, | 44 |
| abstract_inverted_index.network | 51 |
| abstract_inverted_index.obtains | 94 |
| abstract_inverted_index.quality | 142, 182 |
| abstract_inverted_index.realize | 69 |
| abstract_inverted_index.results | 88 |
| abstract_inverted_index.skills. | 76 |
| abstract_inverted_index.studies | 25 |
| abstract_inverted_index.Notably, | 21 |
| abstract_inverted_index.accuracy | 97, 133 |
| abstract_inverted_index.affected | 139 |
| abstract_inverted_index.improved | 177 |
| abstract_inverted_index.previous | 24 |
| abstract_inverted_index.studies. | 86 |
| abstract_inverted_index.tracking | 72, 119 |
| abstract_inverted_index.Cognitive | 0 |
| abstract_inverted_index.Moreover, | 130 |
| abstract_inverted_index.cognitive | 12, 28, 75, 124 |
| abstract_inverted_index.diagnosis | 29 |
| abstract_inverted_index.empirical | 85 |
| abstract_inverted_index.examined, | 157 |
| abstract_inverted_index.knowledge | 14 |
| abstract_inverted_index.regarding | 9 |
| abstract_inverted_index.assessment | 2 |
| abstract_inverted_index.attributes | 107, 156 |
| abstract_inverted_index.conversion | 101 |
| abstract_inverted_index.decreasing | 190 |
| abstract_inverted_index.diagnostic | 1 |
| abstract_inverted_index.illustrate | 89 |
| abstract_inverted_index.increasing | 179 |
| abstract_inverted_index.simulation | 81 |
| abstract_inverted_index.transition | 170 |
| abstract_inverted_index.attributes. | 194 |
| abstract_inverted_index.combination | 111 |
| abstract_inverted_index.development | 15, 121 |
| abstract_inverted_index.educational | 128 |
| abstract_inverted_index.effectively | 118 |
| abstract_inverted_index.information | 8 |
| abstract_inverted_index.measurement | 63 |
| abstract_inverted_index.probability | 171 |
| abstract_inverted_index.situations. | 129 |
| abstract_inverted_index.students’ | 74, 123 |
| abstract_inverted_index.student’s | 11 |
| abstract_inverted_index.traditional | 27, 38 |
| abstract_inverted_index.incorporates | 80 |
| abstract_inverted_index.longitudinal | 42, 71 |
| abstract_inverted_index.psychometric | 19 |
| abstract_inverted_index.classification | 96, 132, 165 |
| abstract_inverted_index.(HMM)/artificial | 49 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 93 |
| corresponding_author_ids | https://openalex.org/A5101873669, https://openalex.org/A5038272179 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I25254941 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.76793186 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |