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WORKPLACE-BASED E-ASSESSMENT TECHNOLOGY FOR COMPETENCY- BASED HIGHER MULTI-PROFESSIONAL EDUCATION MARIEKE VAN DER SCHAAF www.project-watchme.eu @Project_WatchMe This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619349
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Page 1: Workplace-based e-Assessment Technology for Competency ... · WORKPLACE-BASED E-ASSESSMENT TECHNOLOGY FOR COMPETENCY-BASED HIGHER MULTI-PROFESSIONAL EDUCATION MARIEKE VAN DER SCHAAF

WORKPLACE-BASED E-ASSESSMENT TECHNOLOGY FOR COMPETENCY-BASED HIGHER MULTI-PROFESSIONAL EDUCATION

MARIEKE VAN DER SCHAAF

www.project-watchme.eu

@Project_WatchMe

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619349

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1. Utrecht University, NL 2. University Medical Centre Utrecht, NL 3. Szent Istvan University, Hungary 4. University of Tartu, Estonia 5. Universitätsmedizin Charité Berlin, Germany 6. University of California San Francisco, USA 7. Maastricht University, NL 8. Mateum, NL 9. University of Reading, UK 10.Jayway, Denmark 11.NetRom, Romania/NL

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WATCHME’S AIM Improve efficiency and quality of workplace- based feedback and

assessment by means of a mobile electronic portfolio system, that is enhanced:

Conceptually with the concept of Entrustable Professional Activities Technically through Learning Analytics:

Student models that monitor the learners’ development and inform learners and supervisors (based on data of students, supervisors and peers - probabilistic algorithms that learn from new incoming data)

Personalized feedback and visualization of development.

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Develop complex competences

Integrated in context

Demands long learning trajectories in workplace

Deliberate practice: feedback and reflection

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“Well done!” “Pleasure to supervise!” “Reliable candidate” “Poor fund of content knowledge” “Needs lots of supervision”

UNFORTUNATELY, HOW MANY DAILY FEEDBACK PRACTICES LOOK LIKE

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Personalized Feedback that gives (Sadler, 1989; 2010): insight into performance

ability to evaluate and monitor own process

suggestions to fill gap between expected norm and performance

That feeds into learners’ major feedback questions (Hattie & Timperley, 2007):

Where am I going? (goals, feedup)

How am I going? (feedback)

Where to next? (feedforward)

WHAT IS NEEDED

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FROM 90S ONWARDS: ELECTRONIC PORTFOLIOS

Portfolios contain selected evidence of performance and products accompanied by reflections.

• Reflective ‘log’ of trainee over longer time span

• Repository of evidence regarding performance etc

• Different instruments and methods

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Example Digital portfolio, example veterinary education

Aggregation of data points Mini-CEX (clinical evaluation exerc.) Multisource feedback Case reports Knowledge tests Personal development plan

Presenter
Presentation Notes
The ABIM Mini-Clinical Evaluation Exercise (Mini-CEX) is intended to facilitate formative assessment of core clinical skills. It can be used by faculty as a routine, seamless evaluation of trainees in any setting. The Mini-CEX is a 10-20 minute direct observation assessment or “snapshot” of a trainee-patient interaction. Faculty are encouraged to perform at least one per clinical rotation. To be most useful, faculty should provide timely and specific feedback to the trainee after each assessment of a trainee-patient encounter.
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ELECTRONIC PORTFOLIOS MIXED SUCCESS

Underuse of potential data and not very well tailored to worksituations (not timely).

Drawbacks: • Authentic, personal unique character of evidence, difficults scoring.

• Full standardization impossible, asks for interpretation of assessor (no golden standard or external criterion).

• Impacts reliability and validity of the assessment. • Limited impact on learning

• Low motivation of students

Presenter
Presentation Notes
Caused by: Eps are not tailored and well implemented in the workplace Competencies aimed at not well described Underutilized data Students lack skills to work with a portfolio Samenvoegen met volgende dia
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Medical expert

Communicator

Collaborator

Manager

Health advocate

Scholar

Professional

CRITICS COMPETENCY-BASED ASSESSMENT MOST CRITICS COME DOWN TO THE ANALYTIC

APPROACH (E.G. CANMEDS)

Source: Ten Cate

Presenter
Presentation Notes
Canmeds toelichten
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Medical expert

Communicator

Collaborator

Manager

Health advocate

Scholar

Professional

THE ANALYTIC APPROACH

With nursing staff

With family

With patients

With colleagues

With trainees

Source: Ten Cate

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Medical expert

Communicator

Collaborator

Manager

Health advocate

Scholar

Professional

THE ANALYTIC APPROACH

With nursing staff

With family

With patients

With colleagues

With trainees

Consultation

Breaking bad news

Explain medication

With children

With elderly

… Source: Ten Cate

Presenter
Presentation Notes
Subdivisions Risk of checklist approach 142 milestones PBLI on Evalue
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Medical expert

Communicator

Collaborator

Manager

Health advocate

Scholar

Professional

THE SYNTHETIC FRAMEWORK APPROACH

EPA1

EPA2

EPA3

EPA4

EPA5

Source: Ten Cate

Presenter
Presentation Notes
When we use epas a combined task and construct approach occurs. Instead of analytic approach; combine
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ENTRUSTABLE PROFESSIONAL ACTIVITY Task based instead of construct based approach Crucial question: would I entrust this learner unsupervised with this task? (with my sick mother, animal or teach my daughter/son…) An EPA is a task that an individual can be trusted to perform unsupervised, in a given professional context, once sufficient competence has been demonstrated. International Competency-Based Medical Education Collaborators, March 18, 2014

Ten Cate, Chen, Hoff, Peters, Bok & Van der Schaaf, 2015

13

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EXAMPLES ENTRUSTABLE PROFESSIONAL ACTIVITY (EPA)

Conducting patient hand-overs Anesthetic management a patient Conducting a normal, low risk delivery Interviewing adolescents regarding high risk health

behavior

Presenter
Presentation Notes
Watchme entails curriculum innovation
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EXAMPLE EPA

Title of EPA EPA x Routine check-up of the stable adult patient Specification and limitations This EPA includes no more and no less than

1. Measuring vital parameters: heart rate, respiratory rate, temperature, blood pressure, saturation

2. Explaining all actions to the patient 3. Reporting results to the health care team including interpretation,

orally and/or written Context: ambulatory and inpatient setting Targeted transition: first fulltime clinical clerkship to next clerkship Limitations: only with circulatory stable patients of 18 year and older

Most relevant domains of competence X Medical Expert □ Health Advocate X Communicator □ Scholar X Collaborator □ Professional □ Manager

15

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Title of EPA EPA x Routine check-up of the stable adult patient Required experience, knowledge, skills, attitude and behavior

Knowledge: - basic knowledge of anatomy including relevant arteries - normal values of vital parameters Skill: - skill in using necessary devices to measure vital parameters - recognition of stable and unstable patients Attitude and behavior: - professional communication with the patient - proactive alertness in case of adverse events - willingness to ask for help if needed Experience: - all measurements done at least 5 times

Assessment: information sources to assess progress and ground a structural entrustment decision

Observation: satisfactory observation of all measurements at least fully twice by experienced health care professionals (nurse, physician or other) Case-based discussions: one CBD with an qualified health care professional

Entrustment for which level of supervision is to be reached at which stage of training?

Indirect supervision (level 3) ultimately before the transition to the second full time clinical clerkship

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EXAMPLE EPA

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COMBINING THE COMPETENCY-FRAMEWORK WITH ENTRUSTABLE PROFESSIONAL ACTIVITIES

Medical expert

Communicator

Collaborator

Manager

Health advocate

Scholar

Professional

++

+

+

+

+

+

++

++

+

+

+

+

++

++

+

++

++

+

++

+

++

EPA1 EPA2 EPA3 EPA4 EPA5

Assessment Source: Ten Cate

Presenter
Presentation Notes
curriclum
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GROWTH OF ENTRUSTABILITY OVER TIME

training deliberate professional practice

proficient

expert

competent

advanced

novice

Presenter
Presentation Notes
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CURVES OF ONE TRAINEE

training deliberate professional practice

EPA1

EPA4

EPA2

EPA3

EPA5

Compe- tence

thres- hold

Justified entrustment decisions

Presenter
Presentation Notes
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ANOTHER TRAINEE

training deliberate professional practice

EPA1

EPA4 EPA2

EPA3

EPA5

Compe- tence

thres- hold

Justified entrustment decisions

Presenter
Presentation Notes
EPA allows for flexibility: 1. Intra-trainee variation: trainees do not reach competence threshold levels for all professional skills at graduation 2. Inter-trainee variation: residents differ in prior knowledge & skills, learning ability, general attitude 3. Context variation: clinical opportunities, requirements of local practice (epidemiology, facilities, culture), education-mindedness of staff Different from the current one-size-fits-all training; working with EPAs facilitate this flexibility
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COMBINE EPA APPROACH WITH LEARNING ANALYTICS

21

Application of probabilistic student models that enable feedback based on multi sorted assessments

Measurement, collection, analysis and reporting of data about trainees in their contexts, for the purpose of understanding, and optimising learning and the utilising of environments in which it occurs (Solar, 2013)

Personalized feedback

Visualizing learners’ development

Presenter
Presentation Notes
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ELECTRONIC PORTFOLIO

22

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EPASS

Intuitive user interface Monitoring of progress Mobile web application

for entering forms on smartphone or tablet

Presenter
Presentation Notes
Portfolio EPASS supports health professionals in training and their assessors by performing four key tasks. First, it collects specific information about individual learners’ performances and progress in the competencies to be attained at the end of the training programme. Second, it collects specific information about performance and progress in relation to various themes, including statements of fitness to practise and records of activities performed. Third, it collects additional information to provide insight into the learner’s development, including self-evaluations (analyses of strengths and weaknesses, critical incident reports), individual development and learning plans (IDP), academic activities (presentations, publications, dissertation) and educational activities (as both learner and teacher).
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PRINCIPLES OF GOOD FEEDBACK (NICOL AND MACFARLANE-DICK, 2006)

1. Helps clarify what good performance is 2. Facilitates the development of self-assessment in learning 3. Delivers high quality information to learners about their

learning 4. Encourages teacher and peer dialogue around learning 5. Encourages positive motivational beliefs and self-esteem 6. Provides oppurtunities to close the gap between current and

desired performance. 7. Provides information to supervisors

24

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ASSESSMENT ARGUMENTS (MISLEVY, 2006)

What EPAs should be assessed and how does the learner develop during the curriculum?

What performance indicators should be used to gain insight into a learner’s competence?

What instruments should be used to assess the EPAs?

Student Model

Evidence Model

Task Model

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PERSONALIZED FEEDBACK

26

Some users are motivated by competition? Others

find competition demotivating.

Users want to know whether they are on

track.

Users want to be able to highlight and

save useful feedback.

Users would like an overview of weekly,

monthly and yearly goals.

Presenter
Presentation Notes
The bayesian student model must indicate appropriateness of: type of feedback, content of feedback moment of feedback
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1. HELPS CLARIFY WHAT GOOD PERFORMANCE IS (RUBRICS)

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DATA POINTS E-PORTFOLIO

Aggregation of data points Written or electronic test Skills test in simulation (OSCE) Workplace-based assessment

(practice observation) Case-based discussion Multisource feedback Product evaluation

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TYPES OF FEEDBACK

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1. MATCH TO STANDARDS 2. GROUP COMPARISON 3. DEVELOPMENTAL TREND 4. PERSONALIZED RECOMMENDATIONS 5. NARRATIVE FEEDBACK

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MATCH TO STANDARDS - SUPERVISION LEVELS

Ten Cate, Chen, Hoff, Peters, Bok & Van der Schaaf, 2015

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GROUP COMPARISON WITHIN EPASS

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DEVELOPMENT TREND

32

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PERSONALIZED RECOMMENDATIONS

33

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34

MOCK-UP PERSONALIZED FEEDBACK

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NARRATIVE FEEDBACK

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2. 2. FACILITATES THE DEVELOPMENT OF SELF-ASSESSMENT

(REFLECTION) IN LEARNING

Timeline overview: Detailed visualization:

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DANK VOOR JE BELANGSTELLING

www.project-watchme.eu

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BAYESIAN NETWORKS 1) CONSTRUCTION OF GRAPH REPRESENTING QUALITATIVE INFLUENCES OF

SITUATION MODEL 2) ASSIGNMENT OF PROBABILITY TABLES TO EACH NODE IN GRAPH

Presenter
Presentation Notes
Classical ASIAN example
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EXAMPLE XENOS, 2003, P.350

Presenter
Presentation Notes
Building a Bayesian network to model a real-world situation is a two-step process including: (a) construction of a graph representing the qualitative influences of the situation modelled and (b) assignment of probability tables to each node in the graph. The conditional independence statements implied by the graph significantly simplify the elicitation of these probabilities. Many students Longer period of time Changing environment Soft evidence Need for flexible and scalable student modelling
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MEBN ARCHITECTURE

ePASS

databases

Temporary BN Background Machine learning engine

REQUEST

BN Constructor

REPLY

BN Reasoner structure

MEBN Fragment

Store

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Architecture

database

Mobile device

Student Model Database

Learning Analytics Engine

-Online Learning

Just in time Feedback

Aggregated Visualisation

Portfolio system(ePASS)

WPB AssessmentGAME

EPA Matrix

ARCHITECTURE (ZOOMED OUT)

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MULTI-ENTITY BAYESIAN NETWORKS (LASKEY, 2008)

Mfrag as basis concept Mfrag: parameterized Bayesian network fragment

that represents uncertain relationships among a small collection of related hypotheses

Can be instantiated and combined to form complex graphical probability models

Learns from incoming data http://unbbayes.sourceforge.net/ University of Brasil & George Mason University

(Costa & Laskey) Tool for constructing and querying MEBN and other

extended Bayesian network formats

Presenter
Presentation Notes
Probabilistic: deal with soft evidence and uncertainty Online learning of models possible Versatile: models change to situation of the student Scalable: shared fragments of knowledge Semantically labelled

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