Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-10-23

AUTHORS

Juan Zheng, Shan Li, Susanne P. Lajoie

ABSTRACT

This study examined the relationships between clinical reasoning behaviors and diagnostic efficiency in the context of diagnosing a virtual patient in BioWorld, a technology-rich environment designed for medical students to practice clinical reasoning skills. Eighty-two medical students who correctly solved a patient case with Diabetes Mellitus were included in this study. These students were grouped into efficient and less efficient groups based on the time they spent diagnosing the case using k-means clustering. Students’ clinical reasoning behaviors were recorded in log files and further coded as either relevant or irrelevant to the final correct diagnosis. Independent t-tests and sequential pattern mining were then conducted to compare the differences between efficient and less efficient groups. Results revealed that students in the less efficient group collected significantly more irrelevant evidence, ordered more lab tests, and proposed more incorrect hypotheses than efficient students. Moreover, less efficient students demonstrated more disorganized behavioral patterns than efficient students. These findings underscored metacognitive skills in delivering an efficient diagnosis. This study also informs the practice of medical education in terms of the development of expertise, as well as the design of interventions and scaffolding in promoting efficient learning or clinical reasoning. More... »

PAGES

4259-4275

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10639-021-10772-0

DOI

http://dx.doi.org/10.1007/s10639-021-10772-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1142125094


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/13", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Education", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1303", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Specialist Studies In Education", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada", 
          "id": "http://www.grid.ac/institutes/grid.14709.3b", 
          "name": [
            "Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zheng", 
        "givenName": "Juan", 
        "id": "sg:person.016211006025.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016211006025.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada", 
          "id": "http://www.grid.ac/institutes/grid.14709.3b", 
          "name": [
            "Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Shan", 
        "id": "sg:person.016377074012.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016377074012.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada", 
          "id": "http://www.grid.ac/institutes/grid.14709.3b", 
          "name": [
            "Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lajoie", 
        "givenName": "Susanne P.", 
        "id": "sg:person.012155334175.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012155334175.01"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11409-013-9105-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032896075", 
          "https://doi.org/10.1007/s11409-013-9105-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-010-0245-5_2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011396350", 
          "https://doi.org/10.1007/978-94-010-0245-5_2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10459-010-9231-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035334322", 
          "https://doi.org/10.1007/s10459-010-9231-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-91464-0_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104015523", 
          "https://doi.org/10.1007/978-3-319-91464-0_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11165-005-3917-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022547591", 
          "https://doi.org/10.1007/s11165-005-3917-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4419-5546-3_16", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027728066", 
          "https://doi.org/10.1007/978-1-4419-5546-3_16"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11409-017-9174-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090282462", 
          "https://doi.org/10.1007/s11409-017-9174-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11409-014-9112-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013970817", 
          "https://doi.org/10.1007/s11409-014-9112-4"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-10-23", 
    "datePublishedReg": "2021-10-23", 
    "description": "This study examined the relationships between clinical reasoning behaviors and diagnostic efficiency in the context of diagnosing a virtual patient in BioWorld, a technology-rich environment designed for medical students to practice clinical reasoning skills. Eighty-two medical students who correctly solved a patient case with Diabetes Mellitus were included in this study. These students were grouped into efficient and less efficient groups based on the time they spent diagnosing the case using k-means clustering. Students\u2019 clinical reasoning behaviors were recorded in log files and further coded as either relevant or irrelevant to the final correct diagnosis. Independent t-tests and sequential pattern mining were then conducted to compare the differences between efficient and less efficient groups. Results revealed that students in the less efficient group collected significantly more irrelevant evidence, ordered more lab tests, and proposed more incorrect hypotheses than efficient students. Moreover, less efficient students demonstrated more disorganized behavioral patterns than efficient students. These findings underscored metacognitive skills in delivering an efficient diagnosis. This study also informs the practice of medical education in terms of the development of expertise, as well as the design of interventions and scaffolding in promoting efficient learning or clinical reasoning.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10639-021-10772-0", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136394", 
        "issn": [
          "1360-2357", 
          "1573-7608"
        ], 
        "name": "Education and Information Technologies", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "keywords": [
      "efficient students", 
      "medical students", 
      "technology-rich learning environments", 
      "virtual patients", 
      "clinical reasoning skills", 
      "technology-rich environments", 
      "reasoning behavior", 
      "development of expertise", 
      "learning environment", 
      "metacognitive skills", 
      "reasoning skills", 
      "medical education", 
      "students", 
      "clinical reasoning", 
      "efficient learning", 
      "irrelevant evidence", 
      "independent t-test", 
      "skills", 
      "efficient group", 
      "patient cases", 
      "log files", 
      "t-test", 
      "final correct diagnosis", 
      "design of interventions", 
      "BioWorld", 
      "behavioral patterns", 
      "education", 
      "learning", 
      "incorrect hypotheses", 
      "practice", 
      "reasoning", 
      "environment", 
      "expertise", 
      "context", 
      "sequential pattern mining", 
      "study", 
      "sequential mining", 
      "development", 
      "findings", 
      "group", 
      "intervention", 
      "design", 
      "relationship", 
      "terms", 
      "mining", 
      "behavior", 
      "test", 
      "differences", 
      "evidence", 
      "results", 
      "k-means clustering", 
      "pattern mining", 
      "time", 
      "hypothesis", 
      "files", 
      "patterns", 
      "efficient diagnosis", 
      "cases", 
      "lab tests", 
      "efficiency", 
      "diabetes mellitus", 
      "clustering", 
      "correct diagnosis", 
      "diagnostic efficiency", 
      "patients", 
      "diagnosis", 
      "mellitus"
    ], 
    "name": "Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students\u2019 efficiency and behavioral patterns", 
    "pagination": "4259-4275", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1142125094"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10639-021-10772-0"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10639-021-10772-0", 
      "https://app.dimensions.ai/details/publication/pub.1142125094"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_882.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10639-021-10772-0"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10639-021-10772-0'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10639-021-10772-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10639-021-10772-0'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10639-021-10772-0'


 

This table displays all metadata directly associated to this object as RDF triples.

171 TRIPLES      22 PREDICATES      100 URIs      84 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10639-021-10772-0 schema:about anzsrc-for:13
2 anzsrc-for:1303
3 schema:author Nd2c7be18033f4afe8497fde48ed6315e
4 schema:citation sg:pub.10.1007/978-1-4419-5546-3_16
5 sg:pub.10.1007/978-3-319-91464-0_11
6 sg:pub.10.1007/978-94-010-0245-5_2
7 sg:pub.10.1007/s10459-010-9231-x
8 sg:pub.10.1007/s11165-005-3917-8
9 sg:pub.10.1007/s11409-013-9105-8
10 sg:pub.10.1007/s11409-014-9112-4
11 sg:pub.10.1007/s11409-017-9174-1
12 schema:datePublished 2021-10-23
13 schema:datePublishedReg 2021-10-23
14 schema:description This study examined the relationships between clinical reasoning behaviors and diagnostic efficiency in the context of diagnosing a virtual patient in BioWorld, a technology-rich environment designed for medical students to practice clinical reasoning skills. Eighty-two medical students who correctly solved a patient case with Diabetes Mellitus were included in this study. These students were grouped into efficient and less efficient groups based on the time they spent diagnosing the case using k-means clustering. Students’ clinical reasoning behaviors were recorded in log files and further coded as either relevant or irrelevant to the final correct diagnosis. Independent t-tests and sequential pattern mining were then conducted to compare the differences between efficient and less efficient groups. Results revealed that students in the less efficient group collected significantly more irrelevant evidence, ordered more lab tests, and proposed more incorrect hypotheses than efficient students. Moreover, less efficient students demonstrated more disorganized behavioral patterns than efficient students. These findings underscored metacognitive skills in delivering an efficient diagnosis. This study also informs the practice of medical education in terms of the development of expertise, as well as the design of interventions and scaffolding in promoting efficient learning or clinical reasoning.
15 schema:genre article
16 schema:inLanguage en
17 schema:isAccessibleForFree false
18 schema:isPartOf N5c29b25f3e6543e882b9ec3bf3bc411e
19 Nb61f8999047e42e0a4405f52dd24e260
20 sg:journal.1136394
21 schema:keywords BioWorld
22 behavior
23 behavioral patterns
24 cases
25 clinical reasoning
26 clinical reasoning skills
27 clustering
28 context
29 correct diagnosis
30 design
31 design of interventions
32 development
33 development of expertise
34 diabetes mellitus
35 diagnosis
36 diagnostic efficiency
37 differences
38 education
39 efficiency
40 efficient diagnosis
41 efficient group
42 efficient learning
43 efficient students
44 environment
45 evidence
46 expertise
47 files
48 final correct diagnosis
49 findings
50 group
51 hypothesis
52 incorrect hypotheses
53 independent t-test
54 intervention
55 irrelevant evidence
56 k-means clustering
57 lab tests
58 learning
59 learning environment
60 log files
61 medical education
62 medical students
63 mellitus
64 metacognitive skills
65 mining
66 patient cases
67 patients
68 pattern mining
69 patterns
70 practice
71 reasoning
72 reasoning behavior
73 reasoning skills
74 relationship
75 results
76 sequential mining
77 sequential pattern mining
78 skills
79 students
80 study
81 t-test
82 technology-rich environments
83 technology-rich learning environments
84 terms
85 test
86 time
87 virtual patients
88 schema:name Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns
89 schema:pagination 4259-4275
90 schema:productId N141c3ef8f5e0467889afeddb8b51ebc4
91 Na209e6f5bb954860b32a114e72eeb688
92 schema:sameAs https://app.dimensions.ai/details/publication/pub.1142125094
93 https://doi.org/10.1007/s10639-021-10772-0
94 schema:sdDatePublished 2022-05-20T07:38
95 schema:sdLicense https://scigraph.springernature.com/explorer/license/
96 schema:sdPublisher N4d1fe30f3edf408daf70ec37a806a441
97 schema:url https://doi.org/10.1007/s10639-021-10772-0
98 sgo:license sg:explorer/license/
99 sgo:sdDataset articles
100 rdf:type schema:ScholarlyArticle
101 N141c3ef8f5e0467889afeddb8b51ebc4 schema:name doi
102 schema:value 10.1007/s10639-021-10772-0
103 rdf:type schema:PropertyValue
104 N22ee2d535a73448392433dd8ea604da5 rdf:first sg:person.016377074012.89
105 rdf:rest Nd105cf4107f14585b940cd103f2531b3
106 N4d1fe30f3edf408daf70ec37a806a441 schema:name Springer Nature - SN SciGraph project
107 rdf:type schema:Organization
108 N5c29b25f3e6543e882b9ec3bf3bc411e schema:volumeNumber 27
109 rdf:type schema:PublicationVolume
110 Na209e6f5bb954860b32a114e72eeb688 schema:name dimensions_id
111 schema:value pub.1142125094
112 rdf:type schema:PropertyValue
113 Nb61f8999047e42e0a4405f52dd24e260 schema:issueNumber 3
114 rdf:type schema:PublicationIssue
115 Nd105cf4107f14585b940cd103f2531b3 rdf:first sg:person.012155334175.01
116 rdf:rest rdf:nil
117 Nd2c7be18033f4afe8497fde48ed6315e rdf:first sg:person.016211006025.85
118 rdf:rest N22ee2d535a73448392433dd8ea604da5
119 anzsrc-for:13 schema:inDefinedTermSet anzsrc-for:
120 schema:name Education
121 rdf:type schema:DefinedTerm
122 anzsrc-for:1303 schema:inDefinedTermSet anzsrc-for:
123 schema:name Specialist Studies In Education
124 rdf:type schema:DefinedTerm
125 sg:journal.1136394 schema:issn 1360-2357
126 1573-7608
127 schema:name Education and Information Technologies
128 schema:publisher Springer Nature
129 rdf:type schema:Periodical
130 sg:person.012155334175.01 schema:affiliation grid-institutes:grid.14709.3b
131 schema:familyName Lajoie
132 schema:givenName Susanne P.
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012155334175.01
134 rdf:type schema:Person
135 sg:person.016211006025.85 schema:affiliation grid-institutes:grid.14709.3b
136 schema:familyName Zheng
137 schema:givenName Juan
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016211006025.85
139 rdf:type schema:Person
140 sg:person.016377074012.89 schema:affiliation grid-institutes:grid.14709.3b
141 schema:familyName Li
142 schema:givenName Shan
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016377074012.89
144 rdf:type schema:Person
145 sg:pub.10.1007/978-1-4419-5546-3_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027728066
146 https://doi.org/10.1007/978-1-4419-5546-3_16
147 rdf:type schema:CreativeWork
148 sg:pub.10.1007/978-3-319-91464-0_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104015523
149 https://doi.org/10.1007/978-3-319-91464-0_11
150 rdf:type schema:CreativeWork
151 sg:pub.10.1007/978-94-010-0245-5_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011396350
152 https://doi.org/10.1007/978-94-010-0245-5_2
153 rdf:type schema:CreativeWork
154 sg:pub.10.1007/s10459-010-9231-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1035334322
155 https://doi.org/10.1007/s10459-010-9231-x
156 rdf:type schema:CreativeWork
157 sg:pub.10.1007/s11165-005-3917-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022547591
158 https://doi.org/10.1007/s11165-005-3917-8
159 rdf:type schema:CreativeWork
160 sg:pub.10.1007/s11409-013-9105-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032896075
161 https://doi.org/10.1007/s11409-013-9105-8
162 rdf:type schema:CreativeWork
163 sg:pub.10.1007/s11409-014-9112-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013970817
164 https://doi.org/10.1007/s11409-014-9112-4
165 rdf:type schema:CreativeWork
166 sg:pub.10.1007/s11409-017-9174-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090282462
167 https://doi.org/10.1007/s11409-017-9174-1
168 rdf:type schema:CreativeWork
169 grid-institutes:grid.14709.3b schema:alternateName Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada
170 schema:name Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada
171 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...