1 DESCRIPTION, EVALUATION AND MODELING OF LEARNING IN INTERDISCIPLINARY TEAMS Jesús Ibarra, MD, MMEd, MS Dissertation Research Proposal Advisory Committee: Hongbin Wang, PhD (Chair) Dean Sittig, PhD Craig W. Johnson, PhD Joan Engebretson DrPH, AHN-BC, RN James P. Turley, RN, PhD February 1, 2013 School of Biomedical Informatics
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DESCRIPTION, EVALUATION AND MODELING OF LEARNING IN INTERDISCIPLINARY TEAMS
Jesús Ibarra, MD, MMEd, MS Dissertation Research Proposal
Advisory Committee: Hongbin Wang, PhD (Chair)
Dean Sittig, PhD Craig W. Johnson, PhD
Joan Engebretson DrPH, AHN-BC, RN James P. Turley, RN, PhD
February 1, 2013
School of Biomedical Informatics
2 Education
MEDICAL DOCTOR • 1979 - 1986
PEDIATRICS • 1986 - 1990
MASTER OF MEDICAL EDUCATION • 2001 - 2005
MS HEALTH INFORMATICS • 2009 - 2011
DOCTORAL STUDENT HEALTH INFORMATICS • 2009 – current
Practice in the field of transdisciplinary
knowledge acquisition and pediatric care
Leader
Researcher
Physician
University teacher
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Physician
B. S. Biomedical Engineering
(2003)
B.S. Nutrition and well being
(2004)
B.S. Nursing (2006)
B. A. Management. Health Syst.
(2007)
Dentistry (2008)
MdPhD (2008)
M.D. (1976)
Patient, family &
community
Design of new programs: Development of an interdisciplinary health care team
5 Why is this important to Biomedical Informatics?
“Biomedical informatics (BMI) is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health.”
“Faculty should design BMI graduate programs so that every student works collaboratively:
Team effectively with partners within and across disciplines”
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(Kulikowski et al, 2012)
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Goals 6
To understand changes in fields as they integrate
To establish differences in multi, inter and trans disciplinary fields
To understand how integrated teams function and means to improve them
To present my proposed research project
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Outline
1. The case for interdisciplinary team work
2. Identified problems
3. My research Goals Theories Aims Methods Time schedule
4. Questions
8 Real life situations are complex and require teams educated across disciplines
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9 The case for transdisciplinary team work • Growing emphasis in health research,
services, education and policy (Choi, 2007)
• Funding agencies call for research involving multiple disciplines (NIH)
• Hospitals establish multiple disciplinary teams to provide care (Kessler, 2006)
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Education occurs in silos
MDs Nurses Respiratory Therapists Managers
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Problem
• There is a mismatch between education and practice.
• Students learn their professional domains in a mode of silos, but are expected to approach complex problems in real world in a collaborative transdisciplinary fashion.
• Education is being pursued with disciplines apart from each other.
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Problem of terminology
• Common terms: multidisciplinary, interdisciplinary, transdisciplinary (Grossman, 2005)
• Ambiguous definition, interchangeable use (Whitfield, 2004)
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13 Poor communications (Pietroni, 1992) • Each profession
• develops own language, only insiders know
• uses different words with same meaning
▫ cognitive risks
• uses a range of languages
13
14 So, to improve interdisciplinary team learning we must… • Understand the context in which interdisciplinary
team learning takes place
• Find better ways to analyze interdisciplinary team learning
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And I think that (My hypothesis)
There may be certain combinations of elements that can result in more effective interdisciplinary
team learning
16 So, in other words, to improve inter-disciplinary team learning we must…
Understand the learning of interdisciplinary teams G
Team P1. good selection of team members P2. good team leaders P3. maturity and flexibility of team members
Enthusiasm P4. personal commitment of team members
Accessibility P5. physical proximity of team members P6. the Internet and email as a supporting platform
Motivation P7. incentives
Workplace P8. institutional support and changes in the workplace
Objectives P9. a common goal and shared vision
Role P10. clarity and rotation of roles
Kinship P11. communication among team members P12. constructive comment among team members
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Dealing with barriers Strategy Barring the barriers (B)
Team B1. avoid poor selection of disciplines and team members B2. avoid poor process of team functioning
Enthusiasm B3. avoid lack of proper measures to evaluate success of interdisciplinary work B4. avoid lack of guidelines for multiple authorship in research publications
Accessibility B5. avoid language problems Motivation B6. avoid insufficient time for the project
B7. avoid insufficient funding for the project Workplace B8. avoid institutional constraint
Objectives B9. avoid discipline conflicts
Role B10. avoid team conflicts
Kinship B11. avoid lack of communication between disciplines B12. avoid unequal power among disciplines
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(Choi, 2007)
28 To improve interdisciplinary team learning we must…
Student centered (C)
Pre-existiing knowlge (C) Concrete experience (EL) Testing new situation (EL)
Surrogate (KR) Intelligent reasoning (KR)
Pragmatical Comput. (KR)
Integration ladder (ID)
Identification (CDTL)
Formation (CDTL)
Progress (CDTL)
Goal alignment (CDTL)
1. Constructivism 3. Knwl Representation 5. Model Cross-Discip. team learning Th
eory
C
once
pts
Aim
s Va
riabl
es
Met
hods
Understand the learning of interdisciplinary teams G
oals
Model the integration of disciplines
Assess the experience of learning in I.D. teams
4.Integrat. of disciplines 2. Experiential learning
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Innovation
• This research challenges and seeks to understand the proposal of shift of current educational practice, which is predominantly oriented towards one discipline.
• My contribution is a better understanding of how the members of transdisciplinary teams form and function,
• Also, to develop a model of learning in transdisciplinary teams
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Evaluation
• Using an existing survey, it will be applied to a new population of students trained in health disciplines.
• My application fills a gap in interdisciplinary learning in the biomedical and health disciplines field.
• I am comparing the success of the weighted model versus the previous unweighted CDTL by using statistical measures.
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Specific Aims • AIM 1. To establish an understanding of what are
interdisciplinary teams, and how they differ from multidisciplinary and transdisciplinary teams.
▫ Review the literature to sustain the work of interdisciplinary teams, describe promotors and detractors of team activity.
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Specific Aims • AIM 2. To model the concept of interdisciplinary teams
through the application of computational knowledge representation techniques.
▫ Employ knowledge modeling methods, including task analysis, collection and analysis of domain knowledge, use of computational software, and knowledge representation free-ware.
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Specific Aims • AIM 3. To assess the learning of interdisciplinary teams,
to address their cross disciplinary team learning.
▫ Assess the levels of identification, formation, and identification achieved by a type of interdisciplinary team of students
▫ Assess the degree to which teams of interdisciplinary teams Integrate, adapt and are identified to these population
34 To improve interdisciplinary team learning we must…
Aim 1 Understand
Interdisciplinary team learning
Aim 2 Model the concept of
Interdisciplinary teams
Aim 3 Assess how the
members of inter-D teams learn
Student centered (C)
Pre-existiing knowlge (C) Concrete experience (EL) Testing new situation (EL)
Surrogate (KR) Intelligent reasoning (KR)
Pragmatical Comput. (KR)
Integration ladder (ID)
Identification (CDTL)
Formation (CDTL)
Progress (CDTL)
Goal alignment (CDTL)
1. Constructivism 3. Knwl Representation 5. Model Cross-Discip. team learning Th
eory
C
once
pts
Aim
s Va
riabl
es
Met
hods
Understand the learning of interdisciplinary teams G
oals
Model the integration of disciplines
Assess the experience of learning in I.D. teams
4.Integrat. of disciplines 2. Experiential learning
meaning that the publication should examine teaching and learning in interdisciplinary higher education. Peer reviewed. English literature
• Purpose: Establish understanding of how interdisciplinary teams are defined in the literature; how collaboration is conceived; how a team is defined, and which are its stages; what is the evidence of value of teamwork; and what is team based learning.
36 Aim 1. Understand Interdisciplinary team learning • Time: 1992 to 2012
• Search strategy focused on title, abstract, and key words in order to obtain publications with a clear focus on teaching and learning within the context of interdisciplinary higher education.
• Database searched: the Educational Resources Information Centre (ERIC).
37 To improve interdisciplinary team learning we must…
Aim 1 Understand
Interdisciplinary team learning
Aim 2 Model the concept of
Interdisciplinary teams
Aim 3 Assess how the
members of inter-D teams learn
Student centered (C)
Pre-existiing knowlge (C) Concrete experience (EL) Testing new situation (EL)
Surrogate (KR) Intelligent reasoning (KR)
Pragmatical Comput. (KR)
Integration ladder (ID)
Identification (CDTL)
Formation (CDTL)
Progress (CDTL)
Goal alignment (CDTL)
Review of the literature
1. Constructivism 3. Knwl Representation 5. Model Cross-Discip. team learning Th
eory
C
once
pts
Aim
s Va
riabl
es
Met
hods
Understand the learning of interdisciplinary teams G
oals
Model the integration of disciplines
Assess the experience of learning in I.D. teams
Definitions multi-, inter-, trans D (IV) Definition Collaboration (DV) Promotors and detractors (DV) Evidence value teamwork (DV)
4.Integrat. of disciplines 2. Experiential learning
38 Aim 2. Model the concept of Interdisciplinary teams • Application of computational knowledge
representation techniques
• A piece of knowledge explored, in relation to the concept of interdisciplinary health care teams, employing the principles espoused by Harden, Choi, Russell & Norvig, and Brachman & Levesque.
39 Aim 2. Model the concept of Interdisciplinary teams • Computer application exploration ▫ Microsoft® Office Visio® 2007, PRO forma, Tallis,
CLIPS, and Protégé.
• Archetype ▫ PROforma
• Expert system representation ▫ CLIPS
• Ontology ▫ Protégé,
40 Example of Knowledge Representation Expert system representation using CLIPS. Sample of definition of levels and variables that characterize each level.
41 To improve interdisciplinary team learning we must…
Aim 1 Understand
Interdisciplinary team learning
Aim 2 Model the concept of
Interdisciplinary teams
Aim 3 Assess how the
members of inter-D teams learn
Student centered (C)
Pre-existiing knowlge (C) Concrete experience (EL) Testing new situation (EL)
Surrogate (KR) Intelligent reasoning (KR)
Pragmatical Comput. (KR)
Integration ladder (ID)
Identification (CDTL)
Formation (CDTL)
Progress (CDTL)
Goal alignment (CDTL)
Review of the literature
1. Constructivism 3. Knwl Representation 5. Model Cross-Discip. team learning Th
eory
C
once
pts
Aim
s Va
riabl
es
Met
hods
Comp. Knowledge Rep. techniques
Understand the learning of interdisciplinary teams G
oals
Model the integration of disciplines
Assess the experience of learning in I.D. teams
Definitions multi-, inter-, trans D (IV) Definition Collaboration (DV) Promotors and detractors (DV) Evidence value teamwork (DV)
4.Integrat. of disciplines 2. Experiential learning
Multidisciplinary health care team (IV) Integration (DV)
42 Aim 3. Assess how the members of inter-D teams learn
3.1 Analyze the experience and activities of identification.
3.2 Describe how the members of the interdisciplinary team formed and functioned.
3.3 Characterize how the members of an interdisciplinary team made progress
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Setting
• Tecnologico de Monterrey School of Medicine and Health Sciences in Mexico is SACS accredited
• Provides special interdisciplinary learning environment in health sciences undergraduate programs
• Educational programs include Medicine, Nutrition, Nursing, Management of Health Systems, Dentistry, Biomedical Engineering, and Clinical Psychology.
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44 Aim 3. Assess how the members of inter-D teams learn • Population. Students enrolled in the Programs of the School
of Medicine and Health Sciences in Monterrey, Mexico • Sample. Available sample of 194 potential students
representative of an interdisciplinary population, which might include students of ▫ Medicine,
▫ Nursing,
▫ Biomedical engineering,
▫ Nutrition and wellness,
▫ Management of Health Systems,
▫ Dentistry,
▫ Clinical Psychology
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Research Questions • Are higher levels of identification (IV), formation (IV),
and adaptation (IV) associated with higher levels of inter disciplinary team learning (DV)?
• Are there demographic differences in interdisciplinary team learning (DV), by demographic category (IV)?
DV = Dependent variable IV = Independent variable
46 Aim 3. Asses how the members of inter-D teams learn Data collection
• Validated for a theoretical model via statistical analysis,
• High Cronbach alpha reliability coefficient (0.97)
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47 Aim 3. Characterize how the members of inter-D teams learn Questionnaire
24 items in four sections (variables)
• Inter-disciplinary learning (DV)
• Self Assessment (IV)
• Team Formation (IV)
• Team Progress and Accomplishments (IV)
47
(Lei, 2007)
48 Aim 3. Assess how the members of inter-D teams learn Demographic variables • Team name • Project name • Initial major in • Gender • Class level • Work with team member from different majors • Semester in projects • Age
49 Aim 3. Assess how the members of inter-D teams learn
Choose one Description
1.Island of knowledge
I have mastered my discipline, but do not have experience in other disciplines
2. Awareness I am aware of the discipline’s goals and constraints.
3. Appreciation I have begun to build a conceptual framework of the other disciplines, am interested to understand and support the other disciplines’ goals and concepts, and know what questions to ask.
4. Understanding I have developed a conceptual understanding of the other disciplines, can negotiate, am proactive in discussions with participants from other disciplines, provide input before the input is requested, and use the language of other disciplines.
(Adapted from Lei, 2007)
Item: Inter-disciplinary learning (DV)
50 Aim 3. Assess how the members of inter-D teams learn Items: Self-Assessment (IV)
1. I assessed how my abilities fit with project requirement
2. I was able to find information about project requirements
3. I set personal goals for the project. 4. I defined steps to achieve my personal goals.
5. I applied my discipline-specific knowledge.
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(Adapted from Lei, 2007)
51 Aim 3. Assess how the members of inter-D teams learn Items: Team formation (1) (IV) 1. The team defined project goals. 2. Project leader(s) contributed to the success
of the project. 3. Team roles were based upon team members’ discipline-specific
abilities.
4. Team members trusted one another to perform tasks
5. Team members depended upon one another’s contribution.
6. Team members helped one another out.
7. Team members provided peer feedback on a weekly basis.
8. The team asked for feedback from the project partner. (Adapted from Lei, 2007)
52 Aim 3. Assess how the members of inter-D teams learn Items: Team formation (2) 9. The team asked for feedback
10. The team used various communication and collaboration technologies
11. The team used various information technologies to search, organize, retrieve, and store data and information.
12. The team used hardware and software tools
13. Team members were aware of goals and constraints of one another’s disciplines
14. Team members asked questions to understand one another’s disciplines.
(Adapted from Lei, 2007)
53 Aim 3. Assess how the members of inter-D teams learn Items: Team Progress and Completion (IV) 1. Team members aligned personal goals with
project goals.
2. Team members combined the knowledge, techniques, methods, or theories of one another’s disciplines as the project progressed.
3. Team members used language and concepts of one another’s disciplines as the project progressed.
4. The team generated new ideas to solve problems in the project.
5. The team converted new ideas into useful and viable products.
(Adapted from Lei, 2007)
54 Aim 3. Characterize how the members of inter-D teams learn Data collection