Conceptualising a data analytics framework to support targeted teacher professional development

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Professional Development in Education
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Conceptualising a data analytics framework
to support targeted teacher professional
development
Ali Gohar Qazi & Norbert Pachler
To cite this article: Ali Gohar Qazi & Norbert Pachler (05 Nov 2024): Conceptualising a data
analytics framework to support targeted teacher professional development, Professional
Development in Education, DOI: 10.1080/19415257.2024.2422066
To link to this article: https://doi.org/10.1080/19415257.2024.2422066
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RESEARCH ARTICLE
Conceptualising a data analytics framework to support targeted
teacher professional development
Ali Gohar Qazi
a,b
and Norbert Pachler
a
a
Culture, Communication and Media, IOE, UCL’s Faculty of Education and Society, London, UK;
b
Institute for
Educational Development, The Aga Khan University (AKU-IED), Karachi, Pakistan
ABSTRACT
This paper proposes a conceptual framework enabling the development
and adoption of descriptive, diagnostic, predictive and recommendatory
data analytics in teacher professional learning by harnessing some of the
aordances of digital technologies to convert data into actionable
insights. The paper argues for a technology-enhanced approach that
uses data to support teachers in selecting appropriate professional devel-
opment (PD) options to improve their professional practice. The ultimate
goal is to lay the foundations for a robust and adaptable data analytics
framework that could oer tailored PD recommendations based on the
developmental trajectories of individual teachers. The paper analyses
data-supported personalised professional learning as meaning-making
and the appropriation of cultural artefacts within the ‘mobile complex’ -
consisting of structures, agency, and the dynamic interplay between
cultural and technological tools and practices. This study undertakes
a comprehensive literature review to identify key concepts, gaps, and
theoretical insights, informing the development of a data analytics frame-
work. The resultant framework integrates personalisation, teacher agency
and autonomy, contextual relevance, and ethical safeguards into PD
process, aiming to foster a responsive, collaborative, and context-aware
data-supported PD.
ARTICLE HISTORY
Received 2 May 2024
Accepted 17 October 2024
KEYWORDS
Data analytics; teacher
professional development;
data-supported learning;
educational innovation;
conceptual framework
The desirability of a data analytics framework
Teacher professional development is a multifaceted and dynamic process that remains integral to
fostering pedagogical effectiveness and adapting instructional approaches to suit the dynamic needs
inherent within diverse educational contexts (Darling-Hammond et al. 2017, Ávalos 2023). In the ever-
evolving landscape of education, the pursuit of tailored and effective PD opportunities persists as
a continual endeavour, imperative for addressing the multifaceted needs of educators (Nolan and
Molla 2019, Fairman et al. 2023). Within this context, the integration of data analytics emerges as
a potentially innovative opportunity for continuing teacher professional learning (Sampson 2017, Gabbi
2023, Khulbe and Tammets 2023). Defined as an interdisciplinary field leveraging statistical techniques
and computational algorithms to derive insights from data, data analytics can potentially inform
decision-making with real-time evidence, personalise learning experiences and discern nuanced pat-
terns, thereby enabling effective teacher support (Siemens and Gasevic 2012, Siemens 2013, Berendt et al.
2014, Lang et al. 2017, Littlejohn 2017, Ruiz-Calleja et al. 2017, Hakimi et al. 2021).
CONTACT Ali Gohar Qazi ali.gohar.21@ucl.ac.uk Institute for Educational Development (IED), The Aga Khan University
(AKU), Karimabad, Karachi, Sindh 75950, Pakistan
PROFESSIONAL DEVELOPMENT IN EDUCATION
https://doi.org/10.1080/19415257.2024.2422066
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://
creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the
original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow
the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

A review of the literature reveals a plethora of frameworks and models guiding teacher PD
(Kennedy 2005, 2014), yet few of these paradigms incorporate data analytics as a substantive
element. Teacher PD (TPD) is recognised as a complex endeavour (Opfer and Pedder 2011,
Strom and Viesca 2021), manifesting across multiple dimensions – organisational, systemic, and
individual – with each dimension contributing uniquely to the professional growth and evolution of
the practices of educators. Organisational TPD typically involves formal training aligned with
institutional goals, while systemic TPD reflects broader educational policies at larger scales, ensur-
ing compliance with standards and curricula beyond single institutions as well as impacting on
student outcomes measured by international tests and associated league tables. At the individual
level, TPD prioritises teachers’ personal agency and internal motivation, offering activities like self-
directed learning, mentorship and collaborative inquiry, tailored to each educator’s unique profes-
sional development needs and aspirations. These dimensions often overlap and interact, forming
a complex tapestry of professional learning that evolves across different career stages (early-, mid-
and late-career) reflecting heterogeneity inherent in teacher profiles (Coppe et al. 2024). In this
regard, Huberman’s research was pioneering in its recognition of the variability (in terms of needs
and experiences) in teacher professional lives (Huberman 1989a, 1989b). His work underscored the
notion that the professional journey of educators is not monolithic; rather, it is subject to significant
variation and evolution at different stages of their work life. This important understanding suggests
the need for a nuanced approach to PD, one that is acutely aware of, and responsive to the shifting
landscapes of teachers’ professional trajectories (Coppe et al. 2024).
This perspective is echoed in the personalisation literature, which posits that educators, as
lifelong learners, possess distinct professional paths, learning preferences and developmental
needs (Goodwin et al. 2019); therefore, PD should be responsive to these fluctuations. The efficacy
of PD is significantly enhanced when it is customised and tailored to these individual traits, as
teacher learning, akin to student learning, is not a one-size-fits-all endeavour (Schifter 2016).
Personalised PD, therefore, emerges as a critical approach that challenges the traditional, standar-
dised one-size-fits-all models of professional development. By addressing the diverse and context-
specific needs of educators, personalised PD critiques the assumptions of uniformity in teacher
learning and professional growth. It shifts the focus from top-down, institutionally-driven pro-
grammes to a more responsive, teacher-centred model that prioritises individual agency, contextual
relevance, and professional autonomy, thus aligning with critical perspectives that aim to challenge
established hierarchies and promote greater equity. This approach not only empowers teachers but
also recognises the complexities of their roles within varied educational environments and career
phase (Copur-Gencturk et al. 2024), making it a transformative alternative to conventional PD
frameworks. Moreover, scholars have pointed out personalised PD systems can help augment
teachers’ competencies (Ma et al. 2018, Chaipidech et al. 2022).
Notwithstanding, evidence suggests that PD models often default to standardised approaches
that are increasingly focused on furthering institutional/school development plans or are even
designed in response to agendas at the level of school trusts or mission-led organisations. This
can create an environment where PD feels like an exercise in compliance rather than an
opportunity for meaningful professional growth. These PD initiatives, as highlighted by
(Mifsud 2023, Simmie 2023), frequently grapple with an inherent deficiency in adaptability
and customisation, thus impeding their resonance with the intricate array of teachers’ needs,
unique challenges and goals. Similar, sentiments are echoed by numerous scholars (Hargreaves
2000, Kennedy 2014, Desimone and Garet 2015) that the prevailing uniformity in such PD
methodologies not only limits their potential impact but also overlooks the individualised needs
that are intrinsic to effective PD. Indeed, a purely top-down, institutionally-driven approach
risks alienating educators by reducing TPD to a prescriptive process, one that fails to address
the unique challenges teachers face in their specific environments. Consequently, the tension
between individual and institutional priorities in TPD warrants critical examination, as there
exists a risk of data analytics (in this case) being employed to identify and address perceived
2A. G. QAZI AND N. PACHLER

deficits in teachers’ adherence to predetermined professional practices, rather than fostering
genuine growth and transformation. It is, therefore, crucial to strike a balanced approach
between promoting institutional goals versus respecting teacher agency in professional devel-
opment, ensuring that TPD remains a collaborative process that takes into account the differing
needs and contexts of teachers (Darling-Hammond et al. 2017).
A data analytics framework, as proposed in this paper, offers the potential to mediate this
balance by offering personalised insights that could harmonise institutional priorities with tailored
learning pathways for teachers. For instance, by leveraging data-supported insights, institutions can
pursue goals, such as enhancing teaching quality and advancing to educational standards by
tracking trends, identifying gaps, and tailoring interventions based on evidence rather than
assumptions. At the same time, by incorporating teacher input and contextual factors into the data-
supported process – the framework empowers teachers to take ownership of their learning and to
shape their own professional growth. However, critical to the success of this approach is the
ongoing dialogue between institutional priorities and teacher agency. If data is used to impose
rigid benchmarks without considering teacher input, the risk of reducing TPD to a bureaucratic
exercise increases, potentially stifling innovation and creativity. Therefore, the proposed framework
emphasises flexibility and adaptability, enabling teachers to co-construct their professional learning
pathways within the broader institutional vision.
The notion of personalisation, i.e. the adaptation of PD interventions to the specific interests,
preferences and requirements of individual teachers, is central to this paper, as it advocates the use
of data analytics to tailor TPD experiences to individual and/or collective teacher needs and
trajectories (Gabbi 2023, Khulbe and Tammets 2023). Yet, it is essential to consider the compat-
ibility and potential tensions between concepts of personalisation as applied in a data analytics
context and the concept of personalisation in TPD. While data analytics offers the potential for
granular personalisation and visualisation (Bondie and Dede 2024) by examining individual data
points and patterns, relying too heavily on these tools and foci may overlook the intricate nuances
inherent in TPD (Del Pilar Gonzalez and Chiappe 2024). As TPD is inherently a relational and
contextual process, where the pursuit of personalisation through data analytics requires a more
nuanced understanding of teachers’ professional identities, values and the socio-cultural dynamics
of their learning environments. This implies that personalisation within TPD extends beyond mere
individualised quantifiable data points, encompassing the intricate interplay of relational dynamics,
tacit knowledge exchange and the subjective experiences that shape an educator’s professional
journey (Saar et al. 2018).
Thus, data analytics, while supporting the progression of learning (Kubsch et al. 2022), it must
also recognise the inherent value of relational learning and consider capturing physical classroom
data, complemented by qualitative methods such as classroom observations and reflective practices,
to holistically facilitate the translation of learning into meaningful changes in teaching practices.
This implies that the conceptualisation of data-supported personalised PD as a complex and
multifaceted process must be grounded in three complementary theoretical perspectives: beha-
vioural, critical, and sociocultural. Each perspective offers a unique lens for understanding the
complexities of teacher learning and development, and together they provide a holistic view of how
data-supported personalised PD can be most effectively implemented. For instance, a behavioural
perspective provides the tools and metrics for measuring and adapting teacher practice based on
data, while a critical perspective addresses the structural issues – such as power dynamics and
hierarchical inequalities inherent in institutional control and teacher agency – as well as ethical
concerns, particularly regarding assumptions in the use of data within PD. A sociocultural per-
spective then enriches this understanding by situating teacher development within the broader
social and cultural landscape, ensuring that personalised PD is not only data-supported and
equitable but also deeply contextualised and sustainable.
By providing data-supported, personalised insights, data analytics has the potential to empower
teachers in their decision making about their professional learning journeys and it can act as
PROFESSIONAL DEVELOPMENT IN EDUCATION 3

a catalyst for reflective practice (Sergis et al. 2019). Its effective use can be gleaned from examples
where data-driven insights could lead to targeted PD interventions (Hirsch et al. 2018) to accom-
modate the needs, strengths and growth areas of individual teachers. For instance, data analytics can
identify specific areas where a teacher may benefit from further development, such as questioning
techniques or differentiated instruction and recommend resources or courses that address these
needs. We contend that the process of identifying personalised requirements should involve
a balanced approach that combines data-supported insights and teacher agency (Lockton et al.
2020). Since data analytics can provide objective recommendations based on historical teaching
practices and student outcomes, relying solely on externally determined requirements could under-
mine teacher autonomy (Buchanan and McPherson 2019). Therefore, personalised recommenda-
tions should be based on a collaborative process where teachers self-reflect on their PD needs,
complemented by data-supported insights and then contextualise recommendations based on their
professional judgement and unique teaching contexts. The goal is to empower teachers by provid-
ing data-informed guidance while respecting their autonomy, expertise and professional identity,
fostering a co-constructed approach to personalised PD interventions.
The utility of data analytics extends across a spectrum, from being a precursor for personalising
learning experiences to pre-emptively identifying and mitigating academic challenges (Ferguson 2012,
Mangaroska and Giannakos 2018, Khor 2024). However, the utilisation of data analytics in TPD raises
legitimate ethical concerns, potential risks and apprehensions associated with the perception of
excessive monitoring or a surveillance mechanism embedded within data-supported educational
technologies. In this context, Foucault’s (1977) interpretation of the Panopticon becomes particularly
relevant, as systems of constant, invisible observation may foster a ‘Big Brother’ paradigm in which
teachers feel perpetually monitored. This form of surveillance, whether direct or indirect, can impose
control over teachers’ practices, thereby inhibiting innovation and limiting their professional auton-
omy. When educators perceive these technologies as mechanisms of excessive oversight rather than
supportive tools, it can erode trust and diminish the efficacy of TPD initiatives. Consequently, this
undermines open reflection, experimentation, and authentic professional growth. It is, therefore,
crucial to establish robust ethical frameworks and guidelines that prioritise data privacy, transparency
and the autonomy of teachers (Rodríguez-Triana et al. 2016, West et al. 2016). Moreover, issues such
as interpretability of data and the alignment of data-supported recommendations with teachers’
professional contexts are crucial for the advancement of data analytics in TPD. Addressing these
challenges involves not only refining the algorithms and data models but also ensuring that the
insights generated are contextually relevant and respectful of teachers’ professional agency and
autonomy, thereby facilitating a seamless integration of data-supported recommendations within
the multifaceted realities of educational practice.
While data and learning analytics have become powerful tools for making informed decisions
supported by data, there has been little research into how it might shape teacher PD (Oliva-Cordova
et al. 2021, Chiu et al. 2022, Salas-Pilco et al. 2022). Furthermore, there exist only very few, if any,
data analytics frameworks to determine suitable TPD interventions, involving a dual consideration:
informed by specific teachers’ profiles while simultaneously being adaptable to address the unique
requirements of individual teachers. This gap underscores the need for a robust framework that not
only can effectively diagnose and identify teachers’ unique PD needs – by incorporating multiple
data sources and stakeholder inputs – but also identify clusters of teachers facing similar issues,
facilitating the formation of collaborative learning communities that reflect shared cultural and
professional experiences (Lameras and Arnab 2021). The potential for data analytics to inform and
tailor PD initiatives (by synthesising diverse data points), however, presents an untapped oppor-
tunity to support each teacher’s unique professional journey. Hence, this paper addresses the
following fundamental research question:
What are the key characteristics and potential impacts of a data analytics framework designed to support
targeted teacher professional development across varied educational contexts?
4A. G. QAZI AND N. PACHLER

By explicitly posing this question, the paper acknowledges issues around the feasibility and potential
value of such a framework and emphasises the need for drawing upon the existing literature and
theoretical perspectives. This approach allows for a systematic exploration of the challenges,
opportunities and implications associated with integrating data analytics into TPD, in ways that
respect the multifaceted nature of professional learning, the diverse contexts in which teachers
operate, and the need to enhance teacher agency. Engaging with the affordances of data analytics for
TPD requires navigating a wide literature base from different, if cognate disciplines each with their
epistemological, ontological and axiological traditions.
To this end, it seems timely and important to appraise the potential of data analytics to dissect
historical data about professional and pedagogical practice with a view to offering personalised
recommendations to facilitate targeted and impactful PD interventions. As the emerging capabil-
ities and affordances of AI continue to advance, they are likely to transform data analytics from
merely reflecting a static snapshot of past educational interactions to becoming a dynamic, pre-
dictive tool capable of offering highly personalised recommendations (Holmes and Tuomi 2022,
Salas-Pilco et al. 2022). AI technologies can process vast amounts of educational data to identify
patterns and trends that may not be immediately or easily apparent to human analysts. This can lead
to more nuanced and granular insights into teacher professional practice, approaches to teaching
and learning, and learner outcomes (Celik et al. 2022). These AI-driven insights have the potential
to optimise resources and outcomes for teachers (Kusmawan 2023, Dandachi 2024). The descriptive
case study by Tapalova and Zhiyenbayeva (2022), for example, indicates AI’s transformative
potential and predictive capabilities that can forecast individual teachers’ PD requirements, while
its adaptive learning environments can personalise the PD experience (Hwang 2014) to accom-
modate individual teachers’ pace, preferences and goals, thereby potentially enhancing the effec-
tiveness of PD and engagement with PD (Hwang et al. 2020).
As we navigate this transition, it is imperative to recognise that the future trajectory of data
analytics in education sciences will likely be characterised by a shift towards more adaptive,
responsive and personalised educational experiences, underpinned by the sophisticated analytical
power of AI. In this paper, therefore, we aim to conceptualise a data analytics framework that aims
not merely to aggregate data but to translate these insights into actionable and contextually relevant
personalised PD interventions. Integrating AI into our proposed framework could make profes-
sional development more adaptive and personalised, enhancing its overall impact on teacher
growth. In essence, the ability of AI to analyse complex data sets and to provide real-time feedback
can facilitate a more dynamic and responsive approach to teacher PD, one that aligns with the
evolving educational landscape and the specific needs of educators.
Theoretical foundations and review of literature
Data analytics in education: applications, prospects and challenges
Data analytics in education can be defined as the measurement, collection, analysis and reporting of
large sets of data to better understand learners and their learning environments (Siemens and Baker
2012, Siemens 2013, Lang et al. 2017). At its core, educational data analytics involves the systematic
analysis of data generated from diverse educational processes to improve decision-making and
educational practices (Agasisti and Bowers 2017, Ferguson et al. 2019, Wise 2019). This multi-
faceted discipline encompasses a spectrum of applications ranging from personalising learning
experiences to optimising institutional resources and learner outcomes (Maseleno et al. 2018) and it
integrates technical and social/pedagogical dimensions to inform teaching strategies, curriculum
development and learner support services (Siemens 2013).
Amidst the contemporary educational landscape, data analytics has emerged from the burgeon-
ing use of digital resources, learning management systems and various online educational plat-
forms, yielding copious data in diverse formats and at various levels of granularity (Romero and
PROFESSIONAL DEVELOPMENT IN EDUCATION 5

Ventura 2017). The volume and diversity of data generated from these systems cannot be analysed
manually, necessitating analytical tools, such as data/learning analytics, to automatically explore,
analyse and uncover patterns and insights with the intention of benefiting students, teachers and
administrators alike (Siemens and Baker 2012, Dawson et al. 2019). Largely, the field of data
analytics capitalises on the digital footprints/traces and clickstream data, ranging from log files
and online assessments to discussion forum interactions, left by learners as they interact with these
various tools (Gašević et al. 2016, Littlejohn 2017). These traces, when analysed and interpreted
through sophisticated data analytics, can hold real potential to optimise both formal and informal
learning processes traceable and visible to support professionals with their learning (Baker and
Yacef 2009, Gašević et al. 2015, 2017, Littlejohn 2017).
However, the translation of these digital traces into meaningful educational interventions often
remains elusive and frequently encounters obstacles in the way of widespread adoption of data use
(Macfadyen and Dawson 2012, Gašević et al. 2016, Selwyn 2019). One of the primary challenges is
the complexity and purity of data itself; the sheer volume and variety of data points can be
overwhelming (Wolpers et al. 2007, Jones and McCoy 2018) and without a clear framework or
utilisation pathways for analysis, the actionable intelligence or strategies that can be gleaned from
this data are diluted (Boyd and Crawford 2012, Ferguson and Clow 2017). In addition, teachers
frequently harbour doubts regarding the usefulness of data. Even those who are open to engaging
with data may lack the necessary expertise and relevant skills to interpret and apply data analytics
effectively (Rienties et al. 2018, Khulbe and Tammets 2023). This gap highlights the need for
professional development that equips teachers with the necessary skills to harness the power of data
and learning analytics (McKenney and Mor 2015).
While analytical tools for examining learning data hold the theoretical promise of assisting
teachers in utilising data effectively, they frequently fail to live up to expectations (Kitto et al. 2018,
Knight et al. 2020). This shortfall is largely due to the fact that these tools are often developed
without incorporating appropriate theoretical perspectives and without adequately taking into
account the requirements and preferences of end-users, in this case the teachers themselves
(Knight and Shum 2017). For instance, an increasing number of learning analytics dashboards
(LADs), recommender systems and other sophisticated data visualisation mechanisms distils
complex datasets into accessible insights for end-users. Nevertheless, scholarly investigations reveal
a pattern of underutilisation and intermittent engagement with these tools, alongside an absence of
robust evidence substantiating their impact on educational outcomes (Ali et al. 2013, Ferguson et al.
2016). Moreover, the premise of data visualisation is to offer an intuitive understanding of the
information presented. Empirical research by Corrin and De Barba (2015) indicates that while
learners are capable of deciphering feedback from LADs, the translation of this feedback into
concrete actions remains a challenge (Gray et al. 2021). Similarly, Dazo et al. (2017) observed that
teachers, despite their enthusiasm for LADs, encounter difficulties in correlating the data with
pertinent pedagogical issues. This underscores the notion that the mere availability of data analytics
does not inherently confer the ability to act upon the insights derived.
In line with this, the current corpus of scholarly discourse presents several focal points on data
analytics, with particular emphasis on the pragmatic phases of data engagement (Price-Dennis and
Lang 2018, Wise and Jung 2019). This body of work elucidates the critical stages through which
educational data must be processed to yield actionable insights. Furthermore, it underscores the
imperative of adhering to robust legal and ethical frameworks that govern the use of such data.
Paramount among these considerations is the safeguarding of individual privacy, the imperative to
mitigate the risks of data misinterpretation (Selwyn 2019). These elements are integral to optimising
the utility of data analytics within educational contexts. Thus, the integration of data analytics into
existing educational practice not only requires comprehensive technical solutions but also clear
utilisation pathways (aligned with educational goals and learning outcomes) that could guide the
use of data from collection to application. Equally, a large body of literature (Kovanović et al. 2018,
McKenna et al. 2019) suggests that data analytics tools, if properly integrated, could play a pivotal role
6A. G. QAZI AND N. PACHLER

in fostering a culture of reflection and metacognition. Generally, teachers are encouraged to engage in
self-regulatory learning (SRL), a critical component of professional learning, as they become more
aware of their learning habits, progress and areas in need of improvement (Littlejohn et al. 2012). This
shift towards empowering learners with data reflects a broader movement in education to position
learners as active agents in their learning journeys (Laursen 2020, Ndukwe and Daniel 2020).
By merging the insights gleaned from data analytics with the principles of learner-centred
pedagogy, educational institutions can create more responsive and adaptive learning environments
that not only support learners in achieving academic success but also equip them with the self-
regulatory skills necessary for lifelong learning (Kovanović et al. 2018, Marienko et al. 2020). Thus,
the integration of data analytics into educational practices, while fraught with challenges, offers
a potential that extends beyond the optimisation of learning processes. When realised, it can lead to
the cultivation of reflective, self-regulated learners who are better prepared to navigate the complex-
ities of an increasingly complex educational landscape.
Navigating professional development in a Data-Supported Educational Landscape
In the post-pandemic era, marked by digital transformation, the educational sector has witnessed an
exponential increase in the use of digital tools and the proliferation of online and/or blended
learning platforms (Barreiro 2022). This shift has led to the ‘datafication’ of education, where vast
quantities of data are amassed, extending from digitised traditional classroom activities to sophis-
ticated online learning environments. Pangrazio et al. (2022) critique this trend, arguing that the
growing reliance on data as a panacea for educational challenges masks deeper issues related to
control, surveillance, and the reduction of complex educational processes into simplistic metrics.
They are particularly sceptical of the belief that data alone can ‘fix’ educational problems and
caution against its uncritical adoption. This critique is especially relevant in today’s educational
landscape, where teachers in the UK and globally are now immersed in a data-rich context. Their
professional performance and classroom dynamics are continually quantified and scrutinised, often
through annual evaluations based on student outcomes and other pedagogical metrics. This
pervasive datafication – often propelled by New Public Management ideologies and reinforced by
international organisations (such as OECD, PISA, UNESCO, and Governments, etc.,) – and quality
assurance frameworks necessitate a critical discourse on the ‘politics’ of metrics.
The overreliance on quantitative metrics, as a panacea for educational quality and accountability,
may inadvertently eclipse the qualitative, humanistic dimensions of teaching and learning, leading
to a potential ‘tyranny’ of metrics (Muller 2018). Amidst this backdrop, it is imperative to critically
examine the implications of this trend and ensure that the pursuit of data-supported insights does
not overshadow the nuanced realities of educational practice and the diverse experiences of learners
and educators. Additionally, it is crucial to acknowledge that this shift underpinning the datafica-
tion narrative is not inherently beneficial and carries with it significant challenges. For instance,
a challenge often lies not in the data collection but in the timely and effective analysis of such vast
quantities of data. The implementation of predictive learning analytics, for instance, is fraught with
complexities, ranging from ethical considerations around data privacy to the practical difficulties of
integrating these systems within existing educational frameworks (Umer et al. 2023).
Educational data analytics, while playing a pivotal role in the potential refinement of teaching
practices and customisation of instructional strategies, must be approached with a discerning lens
and comprehensive metrics, mindful of the socio-political nuances and the ethical challenges it
presents (Agasisti and Bowers 2017, Hoppe 2017). To elaborate, the transformative possibilities of
data-supported approaches should not be viewed as an end in itself but as a means to enriching our
understanding of the pedagogical process, ensuring that it serves the needs of educators and not the
other way around. Accordingly, to create a comprehensive set of metrics that can guide data-
supported personalised PD recommendations, it is important to consider various dimensions that
PROFESSIONAL DEVELOPMENT IN EDUCATION 7

capture the individual needs of teachers, the effectiveness of PD interventions, and the socio-
cultural context in which these interventions occur.
The metrics (see Table 1) put forward here as a starting point for wider discussion seek to
indicate what has been shown to be beneficial for teachers in the literature surveyed and data that
can be correlated to point teachers towards personalised interventions that they might engage with.
The metrics envisioned here are not exhaustive but indicative and illustrative and seek to provide
a starting point for considering various dimensions that could be measured to understand the role
of data analytics in fostering personalised PD. They are designed to be sensitive to the socio-cultural
context and practices that shape and are shaped by the use of digital technologies and to provide
actionable insights that can enhance the quality and relevance of PD initiatives. Each of these
metrics is tied to a specific type of PD strategy (supported by empirical evidence from the literature)
and possible teacher outcome that is expected to improve through engagement with evidence-based
PD strategies.
Teacher professional development: critical insights, current trends and gaps
At the heart of TPD lies the recognition of its role as a continuous, collaborative, context-specific
process that is essential for the advancement of teachers’ knowledge, competences and values
(Desimone et al. 2002, Guskey 2002, Borko 2004). Avalos (2011) posits that professional develop-
ment of teachers transcends mere acquisition of knowledge; it is an intricate endeavour that
translates this knowledge into pedagogical practices to foster student development. According to
Kazemi and Hubbard 2008 and Opfer and Pedder 2011, teacher professional learning is a complex
process (situational, contextual, ecological), involving many processes, actions and mechanics;
a process which necessitates both cognitive engagement and emotional investment from teacher
both individually and as a collective entity (Tan and Dimmock 2014). This complex PD process
often requires teachers to critically reflect on their own beliefs and convictions and to actively seek
and apply viable alternatives for enhancement or change in their instructional approaches, however,
this is not always the reality. In practice, the extent to which teachers engage in such practice often
depends on the design and mechanisms of the PD programme in which they participate as well as
the level of support provided.
This reality has prompted the field of TPD to explore various theoretical constructs aimed at
elucidating the mechanisms through which teachers acquire new knowledge and adapt their
practices (Postholm 2012). These theoretical constructs span from linear paradigms, such as
those proposed by Guskey (2002), who suggests a linear trajectory of teacher learning as a result
of specific interventions and conditions, to more intricate, meshed notions and/or complexity
thinking acknowledging the complex interplay of factors influencing teacher learning (Taylor
2020). This implies that teacher learning is an inherently dynamic and often unpredictable process,
shaped by a series of iterative and complex web of learning processes (Desimone 2009, Opfer and
Pedder 2011, Basma and Savage 2018, Keay et al. 2019).
In terms of the effectiveness of PD, a robust body of knowledge has been established, delineating
what works to foster teacher learning and what works less well (Garet et al. 2001, Desimone 2009,
Van Veen et al. 2012, Desimone and Garet 2015, Darling-Hammond et al. 2017, Alo et al. 2018,
Kalinowski et al. 2019, Sims and Fletcher-Wood 2021). The literature on teacher professional
development consistently emphasises the need for development opportunities that are not only
continuous but also deeply rooted in the context of teachers’ work environments. Active engage-
ment, collaboration among peers and a focus on both content and pedagogical content knowledge
are frequently cited as key components of successful PD programmes. Equally, studies have
illuminated the importance of sustained and intensive professional development programmes
that go beyond episodic, workshop-based models, advocating instead the embedding of learning
opportunities within teachers’ professional practice (Bautista et al., 2015, Asterhan and Lefstein
2023). These programmes are more likely to have a lasting impact, as they provide ongoing support
8A. G. QAZI AND N. PACHLER

Table 1. Metrics for guiding personalised PD interventions.
Dimension Sub-Dimension Metric Description Indicative Evidence of Effectiveness Data Source(s)
Evidence-Based
PD Practices
Literature-
Supported
Strategies
Collaborative and
Social Learning
Approaches
PD strategies that involve teachers working
together to solve problems, develop teaching
skills, and reflect on their practices through
dialogue and reflection.
Supported by literature indicating that
collaborative PD leads to changes in teacher
practice and student achievement (e.g.
Vescio et al. 2008).
PD programme records,
observation of PD
sessions, teacher surveys.
Inquiry-Based PD
Models
PD strategies that encourage teachers to engage in
inquiry as a means to improve their teaching,
often involving teacher and/or action research.
Research suggests that inquiry-based PD can
enhance teacher reflection and instructional
practice (e.g. Darling-Hammond et al. 2009).
PD programme records,
teacher reflective journals,
interviews with
participants.
Technology
Integration
Training
PD strategies focused on effectively integrating
technology into teaching and learning processes.
Studies show that targeted technology PD can
improve teachers’ technological pedagogical
content knowledge (TPACK) (e.g. Mishra and
Koehler 2006).
PD programme records, pre-
and post-technology
integration assessments,
classroom observations.
Content-Specific PD PD strategies that provide deep content knowledge
in specific subject areas, such as maths or
science.
Literature indicates that content-specific PD is
linked to improved teacher content
knowledge and teaching methods (e.g. Garet
et al. 2001).
PD programme records,
content knowledge
assessments, observation
of content-specific
teaching practices.
Formative
Assessment
Techniques
PD strategies that train teachers in using formative
assessments to guide instruction and provide
feedback.
Empirical evidence supports the use of
formative assessments to improve student
learning outcomes (e.g. Black and Wiliam
1998).
PD programme records,
teacher surveys on
assessment practices,
analysis of student work.
Classroom
Management
Techniques
PD strategies that focus on establishing and
maintaining effective classroom environments.
Research supports the effectiveness of PD in
classroom management for improving
student behaviour and engagement (e.g.
Everston and Weinstein 2006).
PD programme records,
classroom observation
using behaviour checklists,
teacher self-reports.
Differentiated
Instruction
Techniques
PD strategies that equip teachers to tailor
instruction to meet diverse student needs.
Studies demonstrate that differentiated
instruction PD can lead to more inclusive and
effective teaching practices (e.g. Tomlinson
2001).
PD programme records,
teacher lesson plans,
student feedback surveys.
(Continued)
PROFESSIONAL DEVELOPMENT IN EDUCATION 9

Table 1. (Continued).
Dimension Sub-Dimension Metric Description Indicative Evidence of Effectiveness Data Source(s)
Teacher
Outcomes
Based on
Good Practice
Instructional
Practice
Improvement
Changes in instructional strategies and techniques
that align with best practices.
Literature indicates that PD focused on specific
instructional practices can lead to improved
teaching methods (e.g. DeSimone 2009).
Classroom observations
using standardised rubrics,
teacher self-assessment,
peer feedback.
Content Knowledge
Enhancement
Increase in teachers’ depth of understanding in
their subject areas.
Research supports the idea that content-
focused PD improves teachers’ subject matter
knowledge (e.g. Hill et al. 2008).
Pre- and post-content
knowledge assessments,
analysis of lesson plans,
student performance data.
Pedagogical
Content
Knowledge (PCK)
Growth
Development of teachers’ ability to teach content in
effective and meaningful ways.
Empirical studies show that PD can enhance
teachers’ PCK, leading to better student
understanding (e.g. Shulman 1986).
PCK surveys, video analysis of
teaching, student
interviews.
Teacher Self-
Efficacy
Improvement in teachers’ ability to create and
maintain a positive learning environment.
PD that includes classroom management
training is linked to better classroom climate
and student behaviour (e.g. Oliver et al.
2011).
Classroom climate surveys,
behaviour incident
reports, teacher reflections
on classroom
management.
Formative
Assessment
Proficiency
Enhanced skills in using formative assessments to
inform instruction and provide feedback.
Literature suggests that PD on formative
assessment techniques can improve student
learning (e.g. Wiliam 2011).
Analysis of formative
assessment use in the
classroom, teacher
interviews, student
feedback.
Integration of
Technology
Effective use of technology to support teaching and
learning.
Studies indicate that PD on technology
integration can lead to more innovative and
engaging teaching practices (e.g. Ertmer and
Ottenbreit-Leftwich 2010).
Technology use logs, student
engagement metrics,
teacher technology
proficiency assessments.
Reflective Practice
and Professional
Growth
Teachers’ engagement in reflective practice leading
to continuous professional development.
Reflective practice as part of PD is associated
with ongoing teacher learning and
adaptation (e.g. Schön 1983).
Reflective journals,
professional growth plans,
PD participation logs.
(Continued)
10 A. G. QAZI AND N. PACHLER

Table 1. (Continued).
Dimension Sub-Dimension Metric Description Indicative Evidence of Effectiveness Data Source(s)
Teacher
Characteristics
Individual
Teacher
Profile
Professional
Development
Needs
A detailed analysis of each teacher’s professional
development needs based on their current skills,
knowledge gaps, and career goals.
Tailored PD has been shown to be more
effective in meeting individual teacher needs
(e.g. Darling-Hammond et al. 2017).
Surveys, interviews,
performance evaluations.
Demographics Understanding teacher demographics, including –
age, gender, ethnicity/race, years of experience,
educational background, subject specialisation,
geographical location, school type, full-time
/part-time status
Knowing the composition of the teacher
workforce can inform strategies that enhance
the effectiveness of educational programmes
and policies.
Surveys, HR records
Historical
Engagement
with PD
Historical PD Data
Analysis
Review of past PD activities that the teacher has
engaged in, including completion rates and
feedback provided.
Understanding past PD engagement can inform
future PD recommendations (e.g. Desimone
2009).
PD records, learning
management systems
(LMS) data.
PD Preferences Learning Modality
Analysis
Identification of preferred learning modalities. Also,
teacher’s PD orientation, i.e. individual and/or
collaborative learning
PD that accommodates individual learning
preferences can improve learning outcomes
(e.g. Pashler et al., 2008).
PD feedback forms.
Socio-cultural
competence
Cultural
Competency
Evaluation
Assessment of the teacher’s ability to engage with
and teach students from diverse cultural
backgrounds.
Cultural competency is crucial for effective
teaching in diverse classrooms (e.g. Gay
2002).
Self-assessment tools,
student feedback, peer
reviews.
Technology
proficiency
and
Engagement
with digital
technologies
Digital Literacy Technology skills
assessment
Evaluation of teachers’ proficiency with digital tools
and platforms used in PD and teaching.
Technology skills are necessary for effective PD
engagement and classroom integration (e.g.
Tondeur et al. 2017).
Skills assessments, PD
platform usage data.
Online
interaction
patterns
Online behaviour
analysis
Analysis of teachers’ interactions in online PD
platforms, including participation in discussions
and resource utilisation.
Online interactions can provide insights into
teachers’ interests and engagement with PD
content (e.g. Krutka et al. 2016)).
Analytics from PD platforms,
discussion logs.
Usage Frequency Number of times digital tools are used by teachers
in PD activities.
Log data, self-report surveys
Usage Duration Amount of time teacher spent using digital tools in
PD activities.
Log data, self-report surveys
Types of Resources
Accessed
Variety of digital resources accessed by teachers for
professional learning.
Usage tracking, self-report
surveys
Cultural
Practices and
Perceptions
Attitudes Towards
Technology
Integration
Teacher beliefs about the value of technology in PD. Surveys, interviews
Perceived Value of
Tech-Enhanced
PD
Teacher perceptions of the relevance and
effectiveness of technology-enhanced PD.
Surveys, focus groups
Teacher Agency
and
Autonomy
Opportunities for
Choice
Opportunities for teachers to direct their own PD
learning paths.
Teacher agency is argued to be instrumental in
supporting, conditioning and restricting PD
(e.g. Hardman et al. 2023).
PD programme analysis,
teacher surveys
(Continued)
PROFESSIONAL DEVELOPMENT IN EDUCATION 11

Table 1. (Continued).
Dimension Sub-Dimension Metric Description Indicative Evidence of Effectiveness Data Source(s)
Teacher-Led
Initiatives (Self-
directedness)
Number and impact of PD initiatives proposed or
led by teachers.
Programme records, case
studies
Institutional
Support and
Structures
Resource
Availability
Availability of human, technological resources and
infrastructure for PD.
Content focus and active learning over
extended periods of time and within
a professional community in situ are shown
by research to be important variables
(Ingvarson et al. 2005)
Inventory checks,
institutional reports
Policy and
Leadership
Support
Level of institutional support for innovative PD
practices.
Policy analysis, leadership
surveys
Data-driven
decision
making
Responsiveness to
PD
recommendations
Measurement of how teachers respond to data-
driven PD recommendations and the subsequent
actions taken.
Responsiveness to data-driven
recommendations can indicate the relevance
and personalisation of PD (e.g. Mandinach
and Gummer 2016).
PD platform
recommendation tracking,
teacher feedback
Big Data
Analytics
Data Privacy and
Ethics/
Compliance with
Data Protection
Adherence to data privacy laws and regulations in
PD data handling.
Compliance audits, policy
reviews
Transparency and
Consent
Clarity and consent in the collection and use of PD-
related data.
Consent forms, data
management reviews
Identification of
Patterns
Recommendation of
Target Areas and
Development of
PD Plan
Data Visualization
12 A. G. QAZI AND N. PACHLER

for teachers to implement new strategies, reflect on their practice and make iterative improvements.
However, despite the prevailing consensus on the effective PD characteristics, a number of scholars
have raised concerns regarding the empirical foundations of this agreement (Wayne and Youngs
2003, Sims and Fletcher-Wood 2021, Hill et al. 2022). These critiques point to a lack of distinctive
features in the research outcomes, as evidenced by studies that have not identified clear benefits of
PD (Yoon et al. 2007). Furthermore, recent empirical investigations that have intentionally crafted
PD initiatives based on these purportedly effective characteristics have not yielded the expected
improvements when measured against control groups (Garet et al. 2016, Yang et al. 2020). This
suggests that the relationship between PD design and teacher learning outcomes may be more
complex and less predictable than previously thought (Desimone 2023, Fairman et al. 2023).
Contextual factors are widely acknowledged as being critical in the success of PD initiatives
(Sandholtz and Ringstaff 2016, Nawab and Bissaker 2021; Koffeman and Snoek 2019). For instance,
the culture of a school, the support provided by leadership and the broader policy environment can
either facilitate or hinder the professional growth of teachers (Bautista and Ortega-Ruiz 2015).
A school culture, for example, that values continuous learning and provides time for collaboration
is more likely to see positive outcomes from PD efforts (Overstreet 2017, Furner and McCulla
2019). Similarly, leadership that actively supports teacher development by allocating resources and
encouraging innovation can significantly enhance the effectiveness of PD (King 2011, Tayag and
Ayuyao 2020). Despite the clear benefits of context-specific PD, there remains a tendency within the
field to adopt generic approaches that do not adequately address the unique needs of individual
teachers or the specific challenges of their teaching environments (Opfer and Pedder 2011). This
one-size-fits-all approach can lead to a mismatch between the PD offered and the actual needs of
teachers, resulting in suboptimal outcomes (Borko 2004).
As such, there is a growing call within the literature for PD that is differentiated, allowing for
personalisation that takes into account the diverse backgrounds, experiences and pedagogical
challenges faced by teachers (Grierson and Woloshyn 2013, Chaipidech et al. 2022). Moreover,
contemporary perspectives on PD underscore the importance of a social-constructivist
approach, recognising that teachers’ learning is inherently social, shaped by context and evolves
dynamically within a professional community (Kennedy 2014, Boylan et al. 2018). This under-
standing has led to the development of job- and workplace embedded PD models that integrate
practical, collaborative and reflective elements. These models encompass a range of supportive
structures such as instructional coaching, peer observation, action research and inquiry as well
as individualised and customised professional growth plans. Additionally, they leverage tech-
nology through customised online learning platforms and foster collective knowledge-building
within communities of practice.
Intersection of data analytics and teacher PD
The intersection of data analytics and teacher PD represents a potentially transformative nexus in
education offering a data-supported lens through which to refine and enhance pedagogical practices
(Mangaroska et al. 2019, Khor 2024). This fusion facilitates the creation of personalised learning path-
ways – defined as tailored educational experiences that address the individual needs of participants – and
the adoption of adaptive teaching strategies, which adjust instructional methods based on real-time data.
Furthermore, this approach empowers teachers by providing them with real-time feedback, enabling the
identification of learning gaps and allowing for timely interventions (McKenna et al. 2019, Khulbe and
Tammets 2021, Bondie and Dede 2024, Dandachi 2024, Del Pilar Gonzalez and Chiappe 2024).
Agasisti and Bowers (2017) advocate the emergence of the ‘educational data scientist’,
a role that underscores the imperative of harnessing data in educational decision-making
processes. This aligns with the call for data-supported decision-making in education, where
teachers are empowered to leverage data to inform instructional practices, identify student
needs and personalise learning experiences (Datnow and Hubbard 2016). However, the
PROFESSIONAL DEVELOPMENT IN EDUCATION 13

mere presence of data is insufficient; the efficacy of data analytics hinges on its alignment
with sound pedagogical principles and teacher agency (Wise and Shaffer 2015, Wise 2019,
Wise and Jung 2019).
Buckingham Shum et al. (2019) emphasise the importance of ‘human-centred learning
analytics’ that prioritises the needs and agency of teachers within the data-driven landscape.
This resonates with Knight et al. (2014) notion of the ‘middle space’ where data analytics
meets pedagogy, urging for a focus on meaning-making and critical reflection alongside
data interpretation. Data analytics, within TPD, reveals its role in identifying gaps in
teacher knowledge and pedagogical skills, thereby enabling targeted and effective PD
(Sclater et al. 2016). Furthermore, it can facilitate the personalisation of TPD through
using diagnostic and predictive analytics to prescriptive analytics by aligning learning
opportunities with individual teacher profiles and variables, which may include their
teaching experience, subject expertise, pedagogical preferences, disposition, preferred learn-
ing modalities, level of teacher agency (Herodotou et al. 2017, Tapalova and Zhiyenbayeva
2022, Dandachi 2024, Khor 2024). The learning options, however, that data analytics
systems project to an individual need to be sensitive to these teacher variables.
The literature advocates the integration of data analytics into TPD as a means to transcend
traditional one-size-fits-all approaches and to foster a culture of personalised, data-informed
professional learning (Viberg et al. 2018). Nevertheless, current data and learning analytics
systems often lack the involvement of teachers in the recommendation process, leading to
insights that may not align with teachers’ needs or contexts. Consequently, a challenge lies in
crafting a synergistic approach (involving mapping from data ingestion to predicting PD
intervention design) that leverages the technical capabilities of data analytics while remaining
attuned to the nuanced realities of teaching practice (Gašević et al. 2015). Following this,
issues surrounding data collection and generation, such as data purity and representation,
raise concerns about the reliability of data. In the same vein, ethical considerations, as
discussed by Selwyn (2019) and Datnow and Hubbard (2016), emerge as paramount, advocat-
ing for a cautious approach to data privacy, equity, and the mitigation of biases in data
analytics.
Given the variability in teacher profiles and dispositions towards technology and pedagogical
preferences necessitates a personalised approach to TPD that respects individual differences and
contextual factors (Sclater et al. 2016, Rodman 2019, Schachter and Gerde 2019). However, the
literature indicates a scarcity of frameworks specifically designed to guide teacher PD through data-
supported insights. As the field of educational data analytics continues to evolve, it is crucial for
emerging frameworks not only to advance the technical capabilities of data analysis but also, to
address multifaceted challenges, including aligning with the unique trajectories of teacher profes-
sional growth (Ferguson 2012, Ferguson et al. 2016, Ferguson and Clow 2017). A comprehensive
data analytics framework, therefore, should incorporate levels of teacher agency, consider financial
and resource accessibility and ensure systemic feasibility. Such a framework would enable indivi-
dual teachers to respond to recommendations and pursue professional growth within their means
and contexts (Opfer and Pedder 2011).
Moving forward, the current state of data analytics in education reveals a burgeoning interest in
leveraging vast amounts of educational data to inform policy and practice; therefore, research
efforts should prioritise the development of data-supported TPD models that are grounded in
sound pedagogical theory, sensitive to ethical considerations and designed to complement and
enhance teacher agency by providing actionable insights rather than replacing professional judge-
ment and expertise. By fostering collaborative data analysis practices, building teacher data literacy
and ensuring transparency throughout the process, we can harness the power of data analytics to
create a more equitable and effective learning environment for all.
14 A. G. QAZI AND N. PACHLER

Development of the data analytics framework
Theoretical framing
This paper is grounded in the theoretical lens developed by Pachler et al. (2010, 2013) in the
context of their theory building for the field of mobile learning, namely a socio-cultural
ecology which seeks to understand a world in transformation due to an increasing infusion of
(mobile) digital technologies. The theory understands digital technologies, in particular
mobile devices and their services, as cultural resources emerging in a ‘mobile complex’
consisting of structures, agency and cultural practices in constant flux and which are becom-
ing increasingly integrated into (institutionalised) learning, including professional learning.
This theoretical perspective is used to analyse (professional) learning as meaning-making and
as appropriation of cultural artefacts within the socio-cultural field of the mobile complex.
The ecological dimension is understood as the assimilation of learning and practices and
expertise associated with mobile device/technology use in the activity context of everyday life
into the formal curricular learning and contexts of educational institutions. Thus, by embra-
cing the principles of socio-cultural ecology, the proposed framework considers structures,
agency, and the dynamic interplay between cultural and technological tools and practices in
shaping data-supported personalised professional learning. The use of data analytics is framed
within this perspective to explore how data-supported insights can be integrated into mean-
ingful, context-specific professional learning to foster personalised growth and reflective
practices.
Unlike individualistic or behaviourist models that often emphasise isolated skills or outcomes,
a sociocultural ecological perspective, rooted in the work of Vygotsky (1978), emphasises that
learning, including teacher learning, is not merely the outcome of an isolated individual’s cognitive
process, but is an inherently a socially mediated process, deeply embedded within and influenced by
cultural and technological contexts at play (Vygotsky 1978). Building on these insights, data-
supported personalised PD, when viewed through a sociocultural lens, acknowledges that teachers
do not develop in isolation but as members of professional communities that influence and are
influenced by their actions, relationships, practices, and the cultural norms of the educational
environments in which teachers operate.
In practice, this means that data-supported personalised PD should not merely focus on
individual and/or mastering discrete competency development but should also account for the
relational, communal, and culturally specific contexts by facilitating opportunities for peer learning,
collaboration, and collective problem-solving. Teachers learn best when they are able to engage with
colleagues in professional learning communities or (peer) coaching models, where they can share
experiences, strategies, and solutions to common challenges. In this context, data analytics can be
used to identify not only individual needs but also opportunities for collaborative learning. For
instance, teachers with complementary strengths or shared challenges might be brought together in
professional learning communities to co-construct knowledge and engage in joint problem-solving
activities – facilitating group-based learning initiatives. This sociocultural orientation ensures that
data-supported personalised PD does not become an isolated or overly individualistic endeavour
but remains connected to the broader, socially mediated processes that drive teacher growth.
Furthermore, sociocultural theory underscores the importance of cultural relevance in PD. As
teachers work in diverse settings – urban, rural, or international settings each with its own cultural,
social, and educational norms – they may face different challenges that require localised, culturally
responsive professional learning pathways. Therefore, data-supported personalised PD, grounded
in a sociocultural ecology, must tailor learning experiences to these specific contexts, ensuring
relevance and promoting more meaningful and sustained professional growth. For example,
a teacher working in a multicultural classroom in an urban school may require PD focused on
culturally responsive pedagogy, while a teacher in a rural community might benefit from PD
tailored to multi-age teaching or resource-poor environments.
PROFESSIONAL DEVELOPMENT IN EDUCATION 15

Thus, by incorporating an sociocultural ecological approach into the data-supported persona-
lised PD framework ensures that the learning process is not only responsive to individual teacher
needs but also contextually grounded and socially connected. This approach encourages a more
nuanced form of data analytics that not only tracks individual progress but also maps out the social
and cultural networks that influence teacher development. It prioritises collaboration and collective
learning, recognising that professional growth happens within a community and is shaped by
shared experiences and cultural values.
To actualise data within such a socio-cultural ecological framework, we must consider the
broader context in which data is generated and utilised. This means looking beyond the numbers
(not merely a collection of discrete points but a reflection of complex interactions within the
educational ecosystem) to understand the interactions, relationships and cultural norms that
influence educational practices. To put it another way, by adopting socio-ecological perspective,
data becomes a lens through which we view the tapestry of social interactions and cultural
influences on learning. It informs interventions that are not only personalised to the individual
but also attuned to the social fabric of the educational environment. For instance, when analysing
teacher practice data, we must concurrently consider the collaborative and cooperative culture of
the school, the support structures within the professional community and the overarching cultural
values that that underpin educational goals. This holistic approach ensures that data analytics do
not exist in a vacuum but are contextualised within the living, breathing social environment of the
classroom and the wider educational ecosystem.
Building the framework
The proposed data analytics framework builds upon existing literature by integrating insights from
behavioural, critical, and socio-cultural ecological perspectives, which are often treated in isolation
in previous studies on TPD. While prior research has largely focused on data-driven PD models that
emphasise outcomes and performance metrics (e.g. Mandinach and Gummer 2016, Hirsch et al.
2018, Mangaroska et al. 2019, Khor 2024), this framework advances the field by balancing institu-
tional goals with teacher agency and contextual relevance. However, in this paper, we acknowledge
the potential of credible metrics that are congruent with a socio-cultural ecological perspective on
teacher learning. We contend that the effective use of data analytics in professional development
must be underpinned by an appreciation for individual agency, the social nature of learning and the
role of cultural and technological artefacts as mediators of educational practice. By weaving together
the socio-cultural fabric with data analytics, we aim to construct a nuanced, dynamic approach to
professional development; one that is cognisant of the intricate interplay between the cognitive, the
social and the cultural dimensions of learning. We have, therefore, selected and analysed literature
that specifically addresses the intersection of data analytics, digital technologies and professional
development through this lens. The review process involved a systematic search for relevant studies,
theoretical papers and empirical research that explore data analytics in the context of teacher
professional development; therefore, we envisaged use of metrics vis-à-vis professional develop-
ment (see Table 1).
The proposed framework (illustrated in Figure 1) serves as a conceptual roadmap, steering the
development and execution of personalised PD programmes underpinned by data-supported
insights.
The framework depicted in Figure 1 encompasses various aspects related to personalised
PD recommendations for teachers. It highlights the importance of considering characteristics
such as relevance, accessibility, flexibility and effectiveness in designing a successful PD
programme. The inclusion of big data analytics suggests an interest in leveraging large
datasets to gain insights and make data-informed decisions. This aligns with the goal of
identifying patterns and trends to better understand the needs and preferences of teachers.
Additionally, the emphasis on technology proficiency and engagement with digital
16 A. G. QAZI AND N. PACHLER

technologies underscores the desire to enhance teachers’ skills in utilising technology effec-
tively. The framework also acknowledges the significance of teacher agency and autonomy,
emphasising the need to empower teachers and provide them with choices to co-construct
their own PD pathways. Moreover, the framework tailors professional development strategies
to reflect the local needs, resources, and community dynamics. This context-aware approach
enhances the applicability and sustainability of PD by recognising that effective teaching
practices cannot be divorced from the social and cultural realities in which they occur.
Ethical considerations, particularly data privacy, transparency, and compliance with data
protection regulations, are also highlighted as crucial elements. This framework explicitly
addresses these ethical concerns by ensuring that data analytics is used as a supportive tool
rather than a mechanism for oversight or control.
Overall, the proposed data analytics framework stands apart from existing models by integrating
personalisation, teacher agency, contextual relevance, and ethical safeguards into the PD process. It
moves beyond traditional, standardised approaches by offering a more flexible and holistic model
that is responsive to individual needs while being grounded in a critical understanding of the
limitations and risks of datafication in education as well as ensuring that professional learning is
adaptable and relevant to diverse educational settings.
Discussion and conclusion
By harnessing the potential of data, the framework proposed in this paper as a starting point for
discussion and future iteration seeks to offer a nuanced approach to TPD that transcends traditional
methodologies. It aligns with contemporary educational demands for personalisation, responsive-
ness and evidence-based practices. The emphasis of the framework on informing and supporting
individual teacher trajectories or developmental pathways, informed by robust data analysis,
positions it as a model that can adapt to the evolving landscape of teacher needs and educational
technologies. By augmenting AI-supported analyses for recommending different training routes,
PD becomes more tailored to the unique contexts and growth areas of individual educators. This
Figure 1. ‘A possible data analytics framework’.
PROFESSIONAL DEVELOPMENT IN EDUCATION 17

level of customisation enhances the relevance and impact of the training, leading to greater teacher
engagement and motivation.
The proposed framework has significant implications for policy and practice by promoting
a shift from generic PD programmes to tailored approaches that respond to the unique needs of
individual educators. It can guide policymakers and PD providers in allocating resources and
courses more effectively, fostering a culture of continuous learning aligned with broader educa-
tional goals, and ensuring that PD initiatives are both equitable and impactful. This framework is
applicable across diverse educational settings, from resource-rich urban schools in advanced
economies to under-resourced rural schools in low-income countries. By considering local contexts
and data availability, the framework can be customised to meet the specific professional develop-
ment needs of teachers globally. For instance, a school district could use the proposed framework to
analyse teacher data, identifying patterns that reveals areas for further development or highlighting
inconsistencies in teaching practices, while also pinpointing teachers with similar PD needs such as
differentiated instruction or classroom management techniques. The framework would allow for
continuous monitoring and adjustments based on real-time data, providing teachers with targeted
recommendations that align with their professional goals and contexts.
As we look to the future, the next steps in advancing the framework for TPD proposed here,
involve several key phases. Initially, the focus would need to be on developing a prototype – an
operationalizable representation that can be interactively used by educators and administrators.
This prototype would serve as a testbed for the practical application of the principles in order to
refine the user experience. Subsequently, rigorous testing would need to be conducted. This phase is
crucial for gathering feedback, identifying potential issues and assessing the overall usability of the
system. Testing would need to involve a diverse group of educators to ensure that the framework is
inclusive and meets a wide range of PD needs. Future research would also be needed to explore its
scalability, its adaptability to different educational contexts and the long-term outcomes of its
implementation. Continuous iteration and refinement, based on empirical evidence and user
feedback, would be essential.
As practice evolves, it will be key to ensure that data analytics serve as a tool to enhance, rather
than replace, the human elements of teaching and professional growth. Ultimately, the goal is to
foster a human-centric, data-informed culture within TPD that values both quantitative insights
and the qualitative experiences of educators.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Ali Gohar Qazi http://orcid.org/0000-0001-7241-9511
Norbert Pachler http://orcid.org/0000-0002-9770-1836
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