4,880 research outputs found
Standardized testing in Flanders: The role of data coaching and an e-course in the professionalization trajectories for the different feedback users in primary and secondary education
No full textThis study focuses on the role of data coaches in data-based decision-making (DBDM) in education. Interviews and focus groups with academics and practitioners were conducted to understand the needs and challenges regarding data coaching. The study highlights the collaborative nature of data coaching and suggests the involvement of multiple coaches, both internal and external to the school. Distinctions between primary and secondary education contexts are revealed. The practitioners encountered challenges in articulating the specific practices, knowledge, and skills required for a data coach. Despite these
challenges, the study provides valuable insights for future research and contributes to a comprehensive understanding of data coaching in education. This study increases the understanding of the various requirements and preferences related to the role of data coaches in promoting effective databased decision making practices, as data coaches and school teams collaborate closely
Standardized testing in Flanders: The role of data coaching and an e-course in the professionalization trajectories for the different feedback users in primary and secondary education
No full textThis study focuses on the role of data coaches in data-based decision-making (DBDM) in education. Interviews and focus groups with academics and practitioners were conducted to understand the needs and challenges regarding data coaching. The study highlights the collaborative nature of data coaching and suggests the involvement of multiple coaches, both internal and external to the school. Distinctions between primary and secondary education contexts are revealed. The practitioners encountered challenges in articulating the specific practices, knowledge, and skills required for a data coach. Despite these
challenges, the study provides valuable insights for future research and contributes to a comprehensive understanding of data coaching in education. This study increases the understanding of the various requirements and preferences related to the role of data coaches in promoting effective databased decision making practices, as data coaches and school teams collaborate closely
The role of a data coach in supporting data use in school teams: voices of experts and practitioners
No full textA data coach is necessary to support school teams in the use of data to adjust school and classroom practices (Marsh, 2010; 2012). Research shows that depending on the context of the school, the profile of the data coach can take many different forms (Decabooter et al., in preparation). In this study a multifaceted perspective on the role of a data coach is presented by interviewing international academic experts in the field of data use and conducting focus groups with practitioners such as school leaders, teachers and educational support staff. The focus is on the perceived benefits and pitfalls of the different data coach profiles and how they contribute to supporting data use. The results show that experts highlight the importance of coaching skills of a data coach, whereas practitioners prefer a hands-on and practical approach. There are also some similarities between these two groups regarding the combination of an internal-external position
Measuring reflective inquiry in professional learning networks: a conceptual framework
No full textThis study introduces a conceptual framework for measuring reflective inquiry within professional learning networks (PLNs). Reflective inquiry, characterized by collaborative, evidence-based, and dialogic practices, is crucial for fostering professional growth and improving student outcomes. However, current methods for evaluating reflective inquiry often rely on subjective self-reports or labour-intensive discourse analysis, limiting their scalability and objectivity. To address these challenges, this paper explores the potential of AI to identify and assess reflective inquiry in PLN dialogues. The proposed framework evaluates three dimensions: collective dialogue, use of multiple data sources, and depth of reflection. Each dimension is conceptualized along a continuum, allowing for nuanced measurement of interactions ranging from surface-level engagement to critical, evidence-based dialogue. The framework was validated through analysis of 78 hours of PLN sessions, encompassing 2,195 contributions across diverse educational contexts. Results reveal a predominance of surface-level interactions (C1: 64.42%), reliance on informal data (D1: 92.67%), and descriptive reflection (R1: 79.95%). True reflective inquiry, combining high levels of all dimensions (C3, D3, R3), was rare (0.18%), highlighting the need for targeted facilitation. The findings underscore the importance of skilled facilitators in promoting deeper engagement and reflective practices. This framework offers a scalable, objective tool for assessing and enhancing reflective inquiry in PLNs, with implications for professional development and educational improvement. Future research should explore the link between reflective inquiry and changes in teaching practice, as well as strategies to foster deeper reflection among educator
Exploring Learning Outcomes: The Impact of Professional Learning Networks on Members, Schools, and Students
No full textThis study is part of a research project within the Steunpunt Centrale Toetsen in Onderwijs, funded by the Flemish Ministry of Education and Training via budget article Quality of Education [FB0-1FGD2GN-WT]
Co-creatie als tool om tot een gemeenschappelijke taal op vlak van inclusie en diversiteit te komen
No full textHet streven naar kwaliteitsvol inclusief hoger onderwijs in een steeds meer diverse samenleving
staat hoog op de agenda: het realiseren van diverse in-, door- en uitstroom; afstemming tussen de
leeromgeving en de onderwijsbehoeften van alle studenten, en het kritisch evalueren van het
curriculum. Docenten spelen hierin een cruciale rol, in de klas en in samenwerking met andere
professionals zoals studentenbegeleiders, onthaal- en beleidsmedewerkers. Hoewel iedereen
overtuigd is van het belang van kwaliteitsvol inclusief HO, beschikken we niet over een
gemeenschappelijk taal om over inclusie te spreken: wat bedoelen we met inclusie en hoe verhoudt
dit zich tot diversiteit? Wat is diversiteit in de enge en in de brede zin? In deze workshop zetten we
eerste stappen richting een gedeeld kader. We gebruiken arts-based methodieken om concepten
over inclusie en diversiteit scherp te stellen. Dit kan transformerende denkwijzen genereren en
zinvolle veranderingen of conceptualiseringen van taal of informatie bevorderen. In de context van
inclusief HO houdt dit in dat nieuwe concepten ter tafel worden gebracht via de methode ‘Predict
Future Headlines’.
Het streven naar kwaliteitsvol inclusief hoger onderwijs in een steeds meer diverse samenleving
staat hoog op de agenda. Docenten kunnen hierin een cruciale rol spelen door de leeromgeving
zoveel mogelijk te laten aansluiten bij de onderwijsbehoeften van alle studenten, onder meer via het
toepassen van de principes van universal design (Ainscow et al., 2006; de Boer et al., 2011; Meyer et
al., 2014). Een dergelijke krachtige en inclusieve leeromgeving kan echter niet geïmplementeerd
worden door individuele docenten, binnen de context van hun eigen vak of de eigen klaspraktijk
(Sannen et al., 2019). Het vereist samenwerking tussen alle actoren die betrokken zijn bij het
verbeteren en aanpassen van de leeromgeving aan de onderwijsbehoeften van de student, zodat de
student daadwerkelijk wordt opgenomen en zich thuis voelt in het hoger onderwijs en een
verhoogde kans maakt op een succesvolle onderwijsloopbaan (Sannen et al., 2019; Walther-Thomas
et al., 2000). Hoewel iedereen overtuigd is van het belang van kwaliteitsvol inclusief HO, beschikken
we niet over een gemeenschappelijk taal om over inclusie te spreken: wat bedoelen we met inclusie
en hoe verhoudt dit zich tot diversiteit? In deze workshop zetten we eerste stappen richting een
gedeeld kader aangezien het ontbreken van een gemeenschappelijke taal het nastreven van een
gemeenschappelijk doel, in dit geval het realiseren van inclusief onderwijs bemoeilijkt. Vaak
concentreert onderzoek of praktijk zich op de formele en functionele samenwerking tussen
docenten en andere professionals, veel minder op de inhoudelijke component. In deze workshop
nemen we net dit inhoudelijk aspect van samenwerking gericht op kwaliteitsvol inclusief hoger
onderwijs onder de loep.
We gebruiken kunstzinnige methoden om te reflecteren over de principes van inclusie en diversiteit.
Dit soort onderzoeksbenaderingen zijn bij uitstek geschikt om samen te creëren en ervoor te zorgen
dat alle stemmen worden gehoord (zonder verbaal te werken) en perspectieven worden gedeeld
(Emmers, 2019; Fraser & al Sayah, 2011; McKercher, 2020). De focus ligt hierbij op het proces
waarbij open communicatie van belang is (Burkett, 2012). Deze technieken kunnen ook
transformerende denkwijzen genereren die bevorderlijk zijn voor veranderingen of conceptualisaties
Parallelsessies 6 Terug naar inhoudsopgave 358
van taal of informatie, en fantasierijke ruimtes openen voor individuen (Pearson et al., 2018). I Zo
brengen we nieuwe concepten ter tafel over het heden en de toekomst van inclusief onderwijs.
Wat levert de workshop de deelnemers op
We gebruiken U-theorie en de dataverzamelingstechniek "Predict Future Headlines". De workshop
telt vier fasen Observe, Reflect, Act, Convene en Harvest (Grenni et al., 2020; Pearson et al., 2018;
Scharmer, 2009). Deelnemers projecteren zich in de toekomst en bedenken fictieve, krantenkoppen
over inclusief onderwijs (Grenni et al., 2020; Pearson et al., 2018). Deze 'Future Headlines' dienen als
uitgangspunt en inspiratiebron voor gesprekken over de juiste definities en relaties tussen de ideeën
van inclusie en diversiteit. Deelnemers krijgen zo inzicht in een nieuwe participatieve methodiek én
een aanzet tot een nieuw referentiekader aangereikt.
Deelnemers staan open voor een participatieve workshop waarbij ze hun eigen referentiekader
willen (h)erkennen, in vraag stellen en delen
How Professional Learning Networks Can Support Teachers’ Data Literacy: In Conversation with Experts
No full textIn the last decade data-based decision making has been promoted to stimulate school improvement and student learning. However, many teachers struggle with one or more elements of data-based decision making, as they are often not data literate. In this exploratory study, professional learning networks are presented as a way to provide access to data literacy that is not available in schools. Through interviews with scientific experts (n = 14), professional learning networks are shown to contribute to data-based decision making in four ways: (1) by regulating motivation and emotions throughout the process, (2) by encouraging cooperation by sharing different perspectives and experiences, (3) increasing collaboration to solve complex educational problems, and (4) encouraging both inward and outward brokering of knowledge. The experts interviewed have varying experiences on whether professional learning networks should have a homogenous and heterogenous composition, the degree of involvement of the school leaders, and which competencies a facilitator needs to facilitate the process of data-based decision making in a professional learning network
The data coach in action: a systematic review regarding the different tasks, roles and activities
No full textIn the past decades, data-based decision-making (DBDM) to inform practices in education has increased (Mandinach & Schildkamp, 2021). DBDM is a means to maintain and improve the quality of education as well as student learning and achievement (Schildkamp, 2019; Schildkamp et al., 2013; Prenger & Schildkamp, 2018). Despite the growing importance of DBDM, research has pointed out that although teachers and school teams have access to various types of data, they often fail to respond to these data and use it to adjust classroom instruction (Marsh, Bertrand & Huguet, 2015). Furthermore, teachers do not use data in a way that leads to profound changes in instruction or practice because they do not have the necessary skills and knowledge to formulate questions, interpret results and develop instructional responses (Cosner, 2012; Heritage et al., 2009; Marsh et al., 2006; Means et al., 2011; Oláh et al., 2010; Young, 2006; Goertz et al., 2009).
Various studies investigated the factors that enhance DBDM. Human support such as involving data coaches seems to be one of the enabling conditions to promote educator’s use of data and to support the data-team in DBDM (Lachat et al., 2006; Marsh, 2012; Marsh et al., 2006; Marsh et al., 2010; Marsh et al., 2015; Schildkamp et al., 2014). Data coaches can support teachers to become more experts in interpreting data, understanding student thinking and making instructional changes (Marsh et al., 2010; Means et al., 2010).
Although research has highlighted the importance of data coaches, limited research has focused on this role. To further investigate the profile, tasks and roles of data coaches, a systematic literature review was performed. This review has three main objectives. The first goal is to analyse literature on the specific functions, tasks, and roles of a data coach. Next, the study investigates how the professionalization of the data coaches takes form and how the coaches ensure the sustainability of DBDM-practices in schools. Lastly, the study explores how collaboration between data coaches and school leaders takes place since this is still unclear. Exclusion criteria included research that focused on education in kindergarten, nursery schools, higher education, special needs education and research in which the role of the data coach was unclear and/or minimally described. In total nineteen articles were included and analysed using NVivo.
Results show that there are a lot of differences regarding the role of a data coach. Many differences are found, such as the name, the appointment of the role, the effects and the competencies. Similarities are found regarding the range of tasks a data coach fulfills. The coach often takes a guiding and supportive role rather than a steering one. Professionalization of the role is rarely present. Finally, school leaders are often part of the data team and facilitate the data coach's work.
This study investigates which profile data coaches need to have to offer added value. The research reveals the many different interpretations and implementations the role has in practice. Further research is necessary to deepen this role and the necessary professionalization.Cosner, S. (2012). Leading the on-going development of collaborative data practices: Advancing a schema for diagnosis and intervention. Leadership and Policy in Schools, 11(1), 26-65. DOI: 10.1080/15700763.2011.577926
Goertz, M. E., Oláh, L. N., & Riggan, M. (2009). From testing to teaching: The use of interim assessments in classroom instruction. CPRE Research Reports. DOI:10.12698/CPRE.2009.RR65
Heritage, M., Kim, J., Vendlinski, T., & Herman, J. (2009). From evidence to action: A seamless process in formative assessment? Educational Measurement: Issues and Practice, 28(3), 24-31. https://doi.org/10.1111/j.1745-3992.2009.00151.x
Lachat, M. A., Williams, M., & Smith, S.C. (2006). Making sense of all your data, Principal leadership, 7(2), 16-21.
Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842. https://doi.org/10.1016/j.stueduc.2020.100842
Marsh, J.A., Pane, J.F., & Hamilton, L.S. (2006). Making sense of data driven decision making in education: Evidence from recent RAND research, Santa Monica, CA: RAND Corporation. https://doi.org/10.7249/OP170
Marsh, J. A., McCombs, J.S., & Martorell, F. (2010). How instructional coaches support data-driven decision making, Educational Policy, 24(6), 872-907. https://doi.org/10.1177/0895904809341467
Marsh, J. (2012). Interventions Promoting Educators’ Use of Data: Research Insights and Gaps. Teachers College Record, 114(11), 1-48.
Marsh, J.A., Bertrand, M., & Huguet, A. (2015). Using data to alter instructional practice: the mediating role of coaches and professional learning communities, Teachers College Record, 117(4), 1-40.
Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers' ability to use data to inform instruction: Challenges and supports. U.S. Department of Education, Office of Planning, Evaluation and Policy Development.
Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level: From accountability to instructional improvement.
Oláh, L. N., Lawrence, N. R., & Riggan, M. (2010). Learning to learn from benchmark assessment data: How teachers analyze results. Peabody Journal of Education, 85(2), 226-245. DOI: 10.1080/01619561003688688
Prenger, R. & Schildkamp, K. (2018) Data-based decision making for teacher and student learning: a psychological perspective on the role of the teacher, Educational Psychology, 38(6), 734-752, DOI: 10.1080/01443410.2018.1426834
Schildkamp, K., Lai, M. K., & Earl, L. (2013). Data-based decision making in education. Dordrecht: Springer.
Schildkamp, K., Handelzalts, A., Poortman, C., Leusink, H., Meerdink, M., Smit, M., Ebbeler, J., & Hubers, M. (2014). De datateam methode: Een concrete aanpak voor onderwijsverbetering. Garant.
Schildkamp, K. (2019). Data-based decision-making for school improvement: Research insights and gaps, Educational Research, 61(11), 1-17. DOI: 10.1080/00131881.2019.1625716
Young, V.M. (2006). Teachers' use of data: loose coupling, agenda setting, and team norms, American Journal of Education, 112, 521 - 548. DOI: 10.1086/50505
In gesprek met internationale experten: hoe data coaches en lerende netwerken kunnen bijdragen tot datagebruik in scholen
No full textDe beschikbaarheid en het gebruik van data in het onderwijs om klas- en schoolpraktijken bij te sturen is aan een steile opmars bezig. Onderzoek toont aan dat wanneer schoolteams aan de slag gaan met de beschikbare data en op basis hiervan beslissingen nemen, dit kan leiden tot schoolontwikkeling en verbeterde leerprestaties (Schildkamp, 2019; Lai et al., 2014). Hoewel er veel data voorhanden is, hebben leerkrachten en schoolleiders onvoldoende kennis en vaardigheden om op een effectieve manier gebruik te maken van deze data (Marsh et al., 2015). Daarnaast voelen ze zich ook niet vaardig hierin (Verhaeghe et al., 2010). Professionalisering en ondersteuning voor schoolteams is dus noodzakelijk. In de literatuur worden data coaches en lerende netwerken als strategieën naar voren geschoven. Data coaches stimuleren en ondersteunen schoolteams bij het gebruik van data om zo de eigen school- en klaspraktijk bij te sturen (Lachat et al., 2006; Marsh et al., 2015). Lerende netwerken brengen komen scholen en onderwijspartners samen, om zo expertise en kennis rond data te delen en ontwikkelen (Rincon-Gallardo & Fullan, 2016). Hoewel deze strategieën vaak worden geïmplementeerd, is het onduidelijk wat de noodzakelijke taken en competenties van data coaches zijn en hoe partners in lerende netwerken kunnen bijdragen tot het leren van leraren (Prenger et al., 2021). Expert-interviews worden opgezet om deze kenniskloof te dichten.
Deze studie bevat 14 semi-gestructureerde interviews met internationale experten. Deze werden geselecteerd op basis van hun expertise in datagebruik interventies, data coaches en lerende netwerken. Een interviewleidraad werd opgesteld op basis van een voorafgaande systematische literatuurreview. De codering en analyse van de transcripties van de interviews gebeurde in NVivo. De codes werden zowel deductief als inductief opgesteld. Ten slotte werd de intercodeur betrouwbaarheid gemeten om de validiteit en consistentie te garanderen.
De resultaten geven een antwoord op (1) de noodzakelijke competenties van een data coach en (2) hoe lerende netwerken bijdragen aan het gebruik van data.
Experts benoemden volgende noodzakelijke competenties van een datacoach: vakspecifieke kennis, datageletterdheid, interpersoonlijke vaardigheden, kennis over het leren van volwassenen, het hebben van sterke en betrouwbare relaties met het schoolteam, leiderschapsvaardigheden, nieuwsgierigheid, moed om te spreken en coachingsvaardigheden. Experten verschilden echter ook van mening. Sommige experten gaven aan dat de data coach bij voorkeur een school intern persoon is terwijl andere experts het belang van een externe data coach benadrukten.
Experten gaven verschillende redenen aan hoe partners in lerende netwerken kunnen bijdragen aan datagebruik op school verschilde. Deze worden opgedeeld in vier hoofdcategorieën: regulatie van de motivatie, delen van kennis, co-creëren van oplossingen voor problemen en het bouwen van capaciteit op grote schaal.
Deze resultaten dragen bij tot de verdieping van bestaande kennis rond twee vaak geïmplementeerde professionaliseringsstrategieën, namelijk data coaches en lerende netwerken. Beide dragen op een verschillende manier bij aan het gebruik van data in schoolteams om school- en klaspraktijken bij te sturen. Deze twee vormen van ondersteuning en begeleiding kunnen in de praktijk complementair ingezet worden. Verder onderzoek is noodzakelijk om de effectiviteit van de strategieën na te gaan en de onderliggende processen bloot te leggen
Bridging perspectives: Insights from different stakeholders on data coaching in education
No full textThis study focuses on the role of data coaches in data-based decision-making (DBDM) in education. Interviews and focus groups with academics and practitioners were conducted to understand the needs and challenges regarding data coaching. The study highlights the collaborative nature of data coaching and suggests the involvement of multiple coaches, both internal and external to the school. Distinctions between primary and secondary education contexts are revealed. The practitioners encountered challenges in articulating the specific practices, knowledge, and skills required for a data coach. Despite these challenges, the study provides valuable insights for future research and contributes to a comprehensive understanding of data coaching in education. This study increases the understanding of the various requirements and preferences related to the role of data coaches in promoting effective data-based decision making practices, as data coaches and school teams collaborate closely
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