From decisions based on intuition to data-informed decision making Factors hindering the functioning of a data team® in higher eduction Erik Bolhuis, Windesheim University of Applied Sciences, The Netherlands Email: [email protected]. Joke Voogt, University of Amsterdam & Windesheim University of Applied Sciecnes, The Netherlands. Email: [email protected]Kim Schildkamp, University of Twente, The Netherlands. Email: [email protected]Contact details: drs. E.D. Bolhuis, postbus 217, 7500 AE Enschede, The Netherlands. email: [email protected]. http://goo.gl/iXWbzS
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From decisions based on intuition to data-informed decision making
Factors hindering the functioning of a data team® in higher eduction
Erik Bolhuis, Windesheim University of Applied Sciences, The Netherlands Email: [email protected]. Joke Voogt, University of Amsterdam & Windesheim University of Applied Sciecnes, The Netherlands. Email: [email protected]
Kim Schildkamp, University of Twente, The Netherlands. Email: [email protected]
• Teacher Education Colleges —> data use: accountability, part of the curriculum
• Knowledge gab: TE —> data use for school- & instructional improvement
DataInformation that is collected and organized to represent some aspects of the school (Lai & Schildkamp, 2013, p.10).
▪ Input data: e.g. gender, previous school;
▪ Outcome data: e.g. assessments results, written and oral exams, portfolio’s, classroom observations, student surveys, parent interviews, assessment results
▪ Process: e.g. the curriculum, instruction observations
▪ Context data: eg. data on school culture
Ways of data use in education / examples of data:
1 AccountabilityRankings,
drop- out rates
2 School improvement
Drop-out rates, test results, questionnaires, results form
intake
3Instructional improvement
Test results (formative and summative), observations
The data team® method
A data team is:• Teams 6-8 teacher educators and a school leader • Educational problem: grade repetition, low
student achievement • Goals: professional development and school
improvement • Coach guides them through the eight steps (two
years) • Data analysis courses
Case• Dropout in the first study year (HE). In the first year drop-out rates from 55% to
62%.
• Question: what causes drop-out? Is this related to previous education? To gender? To the atmosphere in the class (ambitious study climate)?
• Data: test results, questionnaire (students and supervisors), and the curriculum
• Based on the data, they conclude and develop measurements
Depth of inquiry:
More successful teams (i.e. higher student learning gains) —> more higher level thinking skills (Achinstein 2002; Stokes 2001) —> conversation with a high depth of inquiry.
The depth of inquiry = inquisitive attitude developing new knowledge and taking action based on data, while reviewing each step of the procedure critically (Henry 2012).
The conversations —> reasoning, listening, and underpinning assumptions. Fundamental for making measurements for improvement, and to the construction of team- and individual knowledge (Ikemoto & Marsh 2007).
Depth (Henry, 2012)Depth Participating How Results
No depth Individuals talking
Sending information No shared knowledge
Some depth
Several members involved
Sharing information, experiences, and sources
No shared knowledge base and/or assumptions.
Mean depth
All members are involved
Actively create a new knowledge base
No actively test and sharpen this new knowledge.
Depth All members involved
The discussion focuses on exchanging experiences, information, and opinions.
The discussions are not shallow and lead to a shared explicit knowledge base. Characteristically the dialogue is based on concrete research and/or data.
From literature we know factors influencing data-use (Schildkamp & Kuipers, 2010)
http://goo.gl/iXWbzS
Research questionsWhich factors enable and constrain depth of inquiry
within the data team? 1. Which factors with regard to data and data information systems
enable and prevent depth of inquiry of data team conversations? 2. Which factors on the level of the user enable and prevent depth
of inquiry of data team conversations? 3. Which factors with regard to the assistance of the data team
enable or prevent the depth of inquiry of data team conversations?
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1. Which factors are hindering and promoting factors affecting the depth of the conversations in a data team?
Factors influencing data-useFactors related to data and data-information systems:
Data-information system which provides timely, accurate, relevant, reliable and valid data, data which coincides with the needs
Data related to the perception of the data team members
Factors related to the user:
Data literacy, buy-in/belief, ownership and locus of control Being able to handle cognitive conflicts Clarify prior knowledge Avoid affective conflicts.
Factors related to the organization:
Support from the data coach e.g. conversations skills
Conclusions1.Data —> relate to the level of data literacy 2.Stimulating really use data
3.Clarify prior knowledge;
4.Learn from cognitive conflicts —> clarify which knowledge is conflicting —> manage confusion —> restructure knowledge base;
5.Avoid affective conflicts: but if they do arise, make sure the conflict can be addressed;
6.Data coach —> get insight level of data literacy —> present the data that relate to this level —> and intervene in the conversations to ensure the data team works on a knowledge base together
Discussion• The use of data in the teacher education curriculum,
requires teacher educators, who can improve education based on data;
• Data-use requires active and explicit knowledge-building. Integrating Theory and theory. Should PD pay attention to this process?
• The data coach —> supporting the data team as a team, but also coach to use data to improve their instructional practice?
