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

1-17

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.10.1007/978-1-4419-5546-3_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/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.10.1007/978-3-319-91464-0_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/s11409-014-9112-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013970817", 
          "https://doi.org/10.1007/s11409-014-9112-4"
        ], 
        "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.10.1007/978-94-010-0245-5_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/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/s10459-010-9231-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035334322", 
          "https://doi.org/10.1007/s10459-010-9231-x"
        ], 
        "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"
      }
    ], 
    "keywords": [
      "efficient students", 
      "medical students", 
      "virtual patients", 
      "technology-rich learning environments", 
      "clinical reasoning skills", 
      "technology-rich environments", 
      "reasoning behavior", 
      "development of expertise", 
      "learning environment", 
      "metacognitive skills", 
      "reasoning skills", 
      "medical education", 
      "clinical reasoning", 
      "students", 
      "efficient learning", 
      "skills", 
      "irrelevant evidence", 
      "patient cases", 
      "log files", 
      "efficient group", 
      "design of interventions", 
      "BioWorld", 
      "education", 
      "learning", 
      "behavioral patterns", 
      "incorrect hypotheses", 
      "practice", 
      "reasoning", 
      "environment", 
      "expertise", 
      "context", 
      "sequential pattern mining", 
      "study", 
      "development", 
      "findings", 
      "group", 
      "intervention", 
      "design", 
      "test", 
      "relationship", 
      "behavior", 
      "terms", 
      "mining", 
      "differences", 
      "evidence", 
      "results", 
      "pattern mining", 
      "time", 
      "files", 
      "hypothesis", 
      "patterns", 
      "efficient diagnosis", 
      "cases", 
      "means clustering", 
      "sequential mining", 
      "lab tests", 
      "efficiency", 
      "final correct diagnosis", 
      "clustering", 
      "diabetes mellitus", 
      "correct diagnosis", 
      "diagnostic efficiency", 
      "patients", 
      "diagnosis", 
      "mellitus", 
      "clinical reasoning behaviors", 
      "Students\u2019 clinical reasoning behaviors", 
      "more lab tests"
    ], 
    "name": "Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students\u2019 efficiency and behavioral patterns", 
    "pagination": "1-17", 
    "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": "2022-01-01T18:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_883.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.

166 TRIPLES      22 PREDICATES      99 URIs      83 LITERALS      4 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 Nbf5dbcf5acb841b69eeeafd62340d64d
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 sg:journal.1136394
19 schema:keywords BioWorld
20 Students’ clinical reasoning behaviors
21 behavior
22 behavioral patterns
23 cases
24 clinical reasoning
25 clinical reasoning behaviors
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 intervention
54 irrelevant evidence
55 lab tests
56 learning
57 learning environment
58 log files
59 means clustering
60 medical education
61 medical students
62 mellitus
63 metacognitive skills
64 mining
65 more lab tests
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 technology-rich environments
82 technology-rich learning environments
83 terms
84 test
85 time
86 virtual patients
87 schema:name Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns
88 schema:pagination 1-17
89 schema:productId Nc599792e505b4c9681a4ba5f82c5d69e
90 Nd95c738c1c7343548c418f6aa50029ec
91 schema:sameAs https://app.dimensions.ai/details/publication/pub.1142125094
92 https://doi.org/10.1007/s10639-021-10772-0
93 schema:sdDatePublished 2022-01-01T18:57
94 schema:sdLicense https://scigraph.springernature.com/explorer/license/
95 schema:sdPublisher Nbf98307c190d48a88c0a21aad654dcc8
96 schema:url https://doi.org/10.1007/s10639-021-10772-0
97 sgo:license sg:explorer/license/
98 sgo:sdDataset articles
99 rdf:type schema:ScholarlyArticle
100 N66dc484f1869466fb0b847af9c14d4da rdf:first sg:person.012155334175.01
101 rdf:rest rdf:nil
102 Nbf5dbcf5acb841b69eeeafd62340d64d rdf:first sg:person.016211006025.85
103 rdf:rest Nf5f58c29b63c4e6081f4684e3afa7e8a
104 Nbf98307c190d48a88c0a21aad654dcc8 schema:name Springer Nature - SN SciGraph project
105 rdf:type schema:Organization
106 Nc599792e505b4c9681a4ba5f82c5d69e schema:name doi
107 schema:value 10.1007/s10639-021-10772-0
108 rdf:type schema:PropertyValue
109 Nd95c738c1c7343548c418f6aa50029ec schema:name dimensions_id
110 schema:value pub.1142125094
111 rdf:type schema:PropertyValue
112 Nf5f58c29b63c4e6081f4684e3afa7e8a rdf:first sg:person.016377074012.89
113 rdf:rest N66dc484f1869466fb0b847af9c14d4da
114 anzsrc-for:13 schema:inDefinedTermSet anzsrc-for:
115 schema:name Education
116 rdf:type schema:DefinedTerm
117 anzsrc-for:1303 schema:inDefinedTermSet anzsrc-for:
118 schema:name Specialist Studies In Education
119 rdf:type schema:DefinedTerm
120 sg:journal.1136394 schema:issn 1360-2357
121 1573-7608
122 schema:name Education and Information Technologies
123 schema:publisher Springer Nature
124 rdf:type schema:Periodical
125 sg:person.012155334175.01 schema:affiliation grid-institutes:grid.14709.3b
126 schema:familyName Lajoie
127 schema:givenName Susanne P.
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012155334175.01
129 rdf:type schema:Person
130 sg:person.016211006025.85 schema:affiliation grid-institutes:grid.14709.3b
131 schema:familyName Zheng
132 schema:givenName Juan
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016211006025.85
134 rdf:type schema:Person
135 sg:person.016377074012.89 schema:affiliation grid-institutes:grid.14709.3b
136 schema:familyName Li
137 schema:givenName Shan
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016377074012.89
139 rdf:type schema:Person
140 sg:pub.10.1007/978-1-4419-5546-3_16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027728066
141 https://doi.org/10.1007/978-1-4419-5546-3_16
142 rdf:type schema:CreativeWork
143 sg:pub.10.1007/978-3-319-91464-0_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104015523
144 https://doi.org/10.1007/978-3-319-91464-0_11
145 rdf:type schema:CreativeWork
146 sg:pub.10.1007/978-94-010-0245-5_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011396350
147 https://doi.org/10.1007/978-94-010-0245-5_2
148 rdf:type schema:CreativeWork
149 sg:pub.10.1007/s10459-010-9231-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1035334322
150 https://doi.org/10.1007/s10459-010-9231-x
151 rdf:type schema:CreativeWork
152 sg:pub.10.1007/s11165-005-3917-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022547591
153 https://doi.org/10.1007/s11165-005-3917-8
154 rdf:type schema:CreativeWork
155 sg:pub.10.1007/s11409-013-9105-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032896075
156 https://doi.org/10.1007/s11409-013-9105-8
157 rdf:type schema:CreativeWork
158 sg:pub.10.1007/s11409-014-9112-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013970817
159 https://doi.org/10.1007/s11409-014-9112-4
160 rdf:type schema:CreativeWork
161 sg:pub.10.1007/s11409-017-9174-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090282462
162 https://doi.org/10.1007/s11409-017-9174-1
163 rdf:type schema:CreativeWork
164 grid-institutes:grid.14709.3b schema:alternateName Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada
165 schema:name Department of Educational & Counselling Psychology, McGill University, 3700 McTavish, H3A 1X9, Montreal, QC, Canada
166 rdf:type schema:Organization
 




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


...