Review Reports - Designing Data Science Learning in Initial Teacher Education: The EDUCATE Conceptual Framework

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This article is well-organized, well-researched, clearly written, and addresses a significant and timely need in the field of data science education research. The authors have undertaken a very large task in conducting a scoping review of the current literature to understand how data science, as a field, is described and how it is currently represented in educational frameworks and existing curricula. The authors then use this work as the basis for proposing a guiding framework for teacher education, as it pertains to data science. This contribution has the potential to significantly strengthen teacher education and research about supporting teachers in data science education.

I believe that there are a few adjustments that could significantly strengthen the manuscript and make it more useful for teacher educators, teachers, and researchers.

1) Greater transparency about the connection to literature. In Section 4.1, I appreciated the “integrative synthesis” (line 185) approach. It led to a coherent and streamlined narrative of ten key themes, which were readily understandable.

I recommend adding a table that maps the attribution of each theme to the underlying literature. This table would allow readers to trace specific ideas back to their origins in the literature while preserving the readability of the main text. This suggestion is not meant to question the validity of the synthesis, which seems very thorough. Instead, the intent is to enhance the manuscript’s utility for readers who might want to explore the themes in greater detail.

2) More clarity about and differentiation among the grade levels in focus. In theme 2, what grade-level differences emerged in how data science was described in existing curricula and frameworks? The materials cited in theme 2 focused on different grade levels, with IDS and IDSSP geared toward high school and Ow-Yeong et al. primarily focusing on elementary school. Did the analysis find differences in emphasis and alignment with theme 1 categories, depending on grade level?

Similarly, is the intended focus of the EDUCATE framework all of K-12 education? Are there grade-level differences in the ways you would describe or apply the framework?

3) More methodological detail about creation of the EDUCATE framework. The proposed framework appears thoughtfully designed with four main data processes and 9 practices, along with four pedagogical considerations that accompany the framework. The manuscript states that the framework draws from the core components identified in theme 1 (lines 653–654), and I do see a loose relationship. However, I think readers would benefit from a more explicit description of the translation process from your thematic findings to the final framework. This methodological transparency would help readers understand not just what the framework contains but why it takes this particular form.

For example, some questions that come to mind:

How were the ten core components from Theme 1 reorganized into the four processes and nine practices? What analytical steps or criteria guided this transformation?
1. IMPORTANT: How is the knowledge needed for data scientists different from the knowledge needed by K-12 teachers who wish to support their students’ data science learning? In a few places in the manuscript, you allude to the idea that teachers need some, but not ALL knowledge related to data science (e.g., identifying the teacher population as “non-specialist” learners (line 646–647), which sensibly positions teachers as needing different requisite knowledge that specialists.) It would be insightful to understand your choices around what is and IS NOT included in this framework.
What factors led to organizing the framework around “processes” and “practices” as distinct categories, and how did you determine how the themes were revised and re-organized into these two categories?
How did the four pedagogical considerations emerge from the analysis? How did you determine that these considerations accompanied the main model rather than being an integral part of the model?
Were other organizational structures considered before your final model? How/why did you come to this final model (i.e., what are the advantages/tradeoffs of organizing the model in this way?)

4) Additional guidance in pedagogical considerations. The pedagogical considerations section identifies four important categories for pre-service teachers who are planning data science instruction. However, the current description of these categories does not yet fully deliver on the promise of “pragmatic guidance” (line 805) mentioned in the conclusion. In the current manuscript, this section identifies what teachers should consider, but it could be strengthened by offering more concrete guidance on how to address these considerations. These additions would make the framework more useful for the stated audiences of teachers and teacher educators.

It may be worthwhile to augment this section, because it addresses something that is not captured in the main framework: the pedagogical content knowledge that teachers and teacher educators need to support their learners’ data science learning.

Having said that, I acknowledge that this may be beyond the intended scope of this manuscript. Here are some ideas for strengthening this section without changing the scope too much:

Give a small number of specific examples for each consideration that show how it might translate to classroom practice. For example, under “tools,” what are the tradeoffs between different tools for different age groups?, OR
Reframe the language in the conclusion to match what the section currently provides (e.g., “key considerations for implementation” rather than “pragmatic guidance.”)

Thank you for this thoughtful work. I believe it is a needed contribution to the field.

Author Response

Dear Reviewer,

We sincerely appreciate your efforts in reviewing our manuscript and their your and thoughtful commentary. Our revisions in response to this feedback have enriched the paper considerably. Below in italics are details of how we addressed your feedback. Our changes in the revised manuscript are also in red text.

Review Report (Reviewer 1)

I believe that there are a few adjustments that could significantly strengthen the manuscript and make it more useful for teacher educators, teachers, and researchers.

Response: We thank both reviewers for highlighting the need for greater transparency in Section 4.1 regarding the relationship between the ten synthesized components and the underlying literature. In response, we have created an evidence mapping table that now appears in the revised manuscript. We make reference to the table at the end of the first paragraph in section 4. This table (table 2, pages 9-10) provides clear attribution by linking each component to representative sources already cited in the paper, allowing readers to trace the origins and frequency of key ideas. This addition maintains the coherence of the narrative synthesis while enhancing the manuscript’s rigour, verifiability, and utility for readers who wish to explore specific components in greater detail. The table, itself, is added at the end of section 4.1

Response: We thank the reviewer for raising this important point. We have now added a new paragraph in section 4.2 (paragraph 3, page 11) clarifying the grade-level distinctions in Theme 2 by analysing how different frameworks (e.g., IDS, IDSSP, and Ow-Yeong et al.) emphasise different components of data science based on their target audience. Specifically, we show that elementary-level frameworks emphasise real-world context, visualisation, and ethical engagement, while secondary-level frameworks extend to computational modeling and data infrastructure.

Similarly, is the intended focus of the EDUCATE framework all of K-12 education? Are there grade-level differences in the ways you would describe or apply the framework?

In relation to the EDUCATE framework, this is a valuable question. The EDUCATE framework is designed primarily to support initial teacher education and is therefore focused on preparing pre-service teachers to introduce data science concepts in school settings. While the framework draws from general data science processes and pedagogical considerations that are broadly relevant across K–12, it is not intended to be prescriptive across all grade levels. Instead, it offers a flexible structure that can be adapted to suit learners at different stages of schooling, from upper primary through secondary education. In response to the reviewer’s comment, we have added a new paragraph (paragraph 2, page 20) in section 5.0 clarifying this.

Response: Yes, we agree and have found the questions below useful to us in describing the process. We have added a new subsection (Section 3.4: Designing the EDUCATE Framework, page 6) that explains the full design process.

For example, some questions that come to mind:

How were the ten core components from Theme 1 reorganized into the four processes and nine practices? What analytical steps or criteria guided this transformation?

IMPORTANT: How is the knowledge needed for data scientists different from the knowledge needed by K-12 teachers who wish to support their students’ data science learning? In a few places in the manuscript, you allude to the idea that teachers need some, but not ALL knowledge related to data science (e.g., identifying the teacher population as “non-specialist” learners (line 646–647), which sensibly positions teachers as needing different requisite knowledge that specialists.) It would be insightful to understand your choices around what is and IS NOT included in this framework.

Response: In the new section 3.4 (page 6), we now describe how the ten core themes were reviewed and reorganised based on both theoretical and practical considerations. We explain how we were particularly influenced by the structure of Lee et al. (2022) and the INSTEP program at NC State, both of which informed our distinction between “processes” and “practices.” We also explain how the framework was tailored for non-specialist K–12 educators, focusing on developmentally appropriate skills (e.g., visualisation, data exploration, ethics) while deliberately excluding more advanced or resource-intensive practices (e.g., data infrastructure, advanced programming).

What factors led to organizing the framework around “processes” and “practices” as distinct categories, and how did you determine how the themes were revised and re-organized into these two categories?

Response: We clarify in Section 3.4 (page 6) that this distinction was strongly informed by Lee et al. (2022), who also differentiate between the overarching processes of data science and the embedded practices that support them. We found this structure pedagogically useful, especially when working with teacher educators and pre-service teachers. Our team explored multiple models such as cyclical, nested, and process-aligned but ultimately concluded that treating the practices as flexible and cross-cutting made the framework more adaptable across different grade levels and classroom contexts.

How did the four pedagogical considerations emerge from the analysis? How did you determine that these considerations accompanied the main model rather than being an integral part of the model?

Response: The four pedagogical considerations were derived from both literature and reflection on the realities of classroom practice. We drew inspiration from Bronfenbrenner & Morris (2007) ecological systems theory, positioning these considerations not as core elements of data science itself, but as contextual supports that shape how the EDUCATE framework is implemented in practice. We have mentioned this in section 3.4

Were other organizational structures considered before your final model? How/why did you come to this final model (i.e., what are the advantages/tradeoffs of organizing the model in this way?)

See responses above (especially to part b)

Having said that, I acknowledge that this may be beyond the intended scope of this manuscript. Here are some ideas for strengthening this section without changing the scope too much:

Give a small number of specific examples for each consideration that show how it might translate to classroom practice. For example, under “tools,” what are the tradeoffs between different tools for different age groups?, OR
Reframe the language in the conclusion to match what the section currently provides (e.g., “key considerations for implementation” rather than “pragmatic guidance.”)

Thank you for this thoughtful work. I believe it is a needed contribution to the field.

Response: We appreciate the reviewer’s thoughtful suggestions for enhancing the pedagogical considerations section to better support classroom application. In response, we made two targeted changes:

(a) Concrete examples added: We have expanded Section 6 (Beyond the Framework: Practical Considerations for Implementation – pages 24-25) to include brief, illustrative examples for each of the four pedagogical considerations (Context, Key Ideas, Tools, Assessment). These examples are intended to highlight how these considerations might translate into classroom decisions across different grade levels, while remaining within the conceptual scope of the paper. Each example is integrated under its respective subheading in Section 6.

(b) Language revised in conclusion: We revised the conclusion to replace the phrase “pragmatic guidance” with “key considerations for implementation” to more accurately reflect the level of support provided in the manuscript. This reframing ensures alignment between the main body and the concluding remarks.

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you to the authors for an interesting and timely contribution that addresses the growing need for coherent data science preparation in initial teacher education and for reviewing a broad literature base into a practical conceptual framework for teachers. A clear strength of the manuscript is its ambition to combine database searching with an expert-informed identification strategy and to connect the review output to a concrete design product, namely the EDUCATE conceptual framework and related enactment considerations. The topic is relevant to curriculum design and teacher educator practice, and the manuscript contains many useful descriptions of data science processes and practices that could become valuable once the evidence base is presented transparently and systematically.

However, this version of the paper has some flaws that should be resolved before publication.

The main concerns relate to methodological transparency and to the extent to which the paper currently meets expectations for a systematic scoping review. The PRISMA reporting is incomplete because the flow implies that 1,531 records were screened, 134 were eligible for full-text review, 88 additional studies were added (or what other action was taken within these 88 articles?), and 222 were then analysed, yet the reference list shows only 53 sources. The diagram and text do not report full-text exclusions, deduplication, or overlap between sources, which makes the selection pathway non-auditable.

The manuscript also lacks explicitly formulated research questions, as the “guiding questions” appear later as internal analytic prompts rather than as formal review questions stated upfront to guide inclusion criteria, synthesis structure, and reporting.

Section 4 reads predominantly as a narrative synthesis because the ten components in 4.1 are presented “as an integrative synthesis rather than through attribution to individual sources”, and therefore the reader cannot verify which sources support each component, how frequently components occur, or where definitions diverge. To reach a systematic level, Section 4 needs a consolidated evidence table (or tables) that maps each component or term to its source, with citations. The operational definition or interpretation in that source, along with its context, and an analogous comparative synthesis table, are also required for Section 4.2 across the cited frameworks and curricula.

Section 5 requires a clearer demonstration of the authors’ novelty and added value. The EDUCATE framework is presented as reasonable and intuitive, yet the manuscript does not show how it differs from or advances existing frameworks discussed earlier, and Table 2 and the accompanying text (Section 4) do not clearly indicate whether the descriptions are derived from specific sources, are a synthesis, or are the authors’ original propositions.

As a result, the absence of a substantial Discussion section further prevents the manuscript from articulating its contribution through explicit comparison with existing approaches, justification of design choices, implications, and limitations.

The final section is titled “Summary and Conclusions”, but in practice it largely repeats earlier material, and the paper would benefit from a dedicated Discussion followed by a concise Conclusions section without redundant summarisation.

In conclusion, the manuscript has a relevant topic and a potentially useful framework, but it currently does not meet key expectations for systematic scoping review reporting and transparency in synthesis, and the contribution of the EDUCATE framework has not yet been demonstrated through explicit comparison and critical discussion. The paper can be published after revision.

Comments on the Quality of English Language

The quality of English requires professional proofreading to ensure a consistent academic style. The text contains recurring non-native patterns, such as overly long, syntactically overloaded sentences, occasional agreement and phrasing issues, and a highly generic academic register that reduces precision and sometimes obscures meaning.

Author Response

Dear Reviewer,

We sincerely appreciate your efforts in reviewing our manuscript and their your and thoughtful commentary. Our revisions in response to this feedback have enriched the paper considerably. Below in italics are details of how we addressed your feedback. Our changes in the revised manuscript are also in red text.

Review Report (Reviewer 2)

However, this version of the paper has some flaws that should be resolved before publication.

Response: Thank you for highlighting the need for greater methodological clarity in our review process. In response, we have substantially revised Section 3 (Methods, pages 3-6) to offer a more detailed account of our systematic review process, including screening procedures, inclusion/exclusion criteria, reviewer calibration, and full-text review protocols. We now clarify that:

A total of 1,531 records were retrieved, and 327 duplicates were removed using Zotero, leaving 1,204 records for screening.
134 articles were retained for full-text review, of which 21 were excluded based on refined criteria.
An additional 88 studies were identified through hand-searching, citation chaining, expert recommendations, and grey literature. Of these, 27 were duplicates, and 39 were excluded, leaving 22 additional studies for inclusion.
This yielded a final analysis pool of 135 studies.

All 135 papers were read and used in thematic synthesis. However, approx. 60 studies made unique contributions to the development of the ten themes (Section 4) and the EDUCATE framework (Section 5) and are therefore cited in the manuscript. The remaining studies helped corroborate and contextualize findings but were not directly cited.

We have also updated the PRISMA diagram (page 5) and accompanying narrative to reflect these steps more transparently and to make the selection process auditable. Moreover, we have added the expert-elicitation process to the PRISMA diagram in an effort to synthesise/present both approaches to gather readings. As mentioned earlier in this document, a new evidence mapping table (see Section 4.1) has also been added, which links the themes directly to their underlying sources.

Response: Thank you for this important and insightful suggestion regarding the attribution of sources and the systematic nature of Section 4. In response to your feedback, we have undertaken the following revisions:

In Section 4.1, we have created and included a consolidated evidence mapping table (table 2, pages 9-10) that identifies and attributes relevant literature to each of the ten synthesised components of data science. This table links each component to its supporting sources already cited in the manuscript, allowing readers to trace contributions back to the literature base.
While we have focused primarily on mapping citations to each theme, we did not undertake a full frequency count or extract the exact operational definitions and contextual settings from each source, as we judged this to be beyond the scope of the current paper. However, the sources provided are selected for their clarity and representativeness and are intended to offer a strong starting point for readers seeking more depth.
Regarding Section 4.2, we appreciate the suggestion to construct an analogous comparative synthesis across frameworks and curricula. While we have not created a second formal table here, we have added clarifications in the narrative to ensure the grade levels and contexts of the cited frameworks (e.g., Ow-Yeong et al. for elementary, IDS and IDSSP for high school) are more transparent, and to highlight areas of overlap or divergence more explicitly.

Response: Thank you for this feedback, we have added a new section in section 5 (pages 23-24) titled ‘5.1 Positioning the EDUCATE Framework in the Landscape of Data Science Education, where we specifically address the novelty and added value of the EDUCATE framework identifying three key innovations from EDUCATE that advance the field of data science education for teachers.

Response: Thank you for this observation. We appreciate the suggestion to restructure the final section. Indeed, we have added updates in the conclusion that have taken into account these new additions to the manuscript. However, in light of the substantial additions made throughout the manuscript - including enhanced methodological detail, an expanded pedagogical considerations section, and new explanatory text around the EDUCATE framework - we believe that a brief recap in the form of a summary remains helpful for readers. It offers a synthesised overview of the key contributions and reinforces the core messages of the paper. For this reason, we have retained the structure of the section as originally conceived. We hope this is acceptable within the broader context of the revised manuscript.

Comments on the Quality of English Language

Response: We have had the paper, and all revisions, carefully proofed by a native English speaker.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for the thoughtful revisions. I believe that this version has been strengthened enough to warrant publication.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript has been substantially strengthened through revision. The core methodological flaws have been rectified, and the framework's novelty is now well articulated.