Computer science and non-computer science faculty members' perception on teaching data science via an experiential learning platform
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI can provide instructors and students with new, rich, and personalized materials, contributing to curriculum design and development and facilitation of course preparation.
“Both studies conducted focus group interviews, and revealed that AI, at curriculum levels, could provide instructors and students with new, rich, and personalized materials, contributing to curriculum design and development and facilitation of course preparation.”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Chen et al., 2023
Inconsistent definitions of data science, inadequate team support, and lack of collaboration platforms were major challenges for faculty members in data science.
“Chen et al. (2023) interviewed 16 faculty members in data science and revealed that inconsistent definitions of data science, inadequate team support, and lack of collaboration platforms were major challenges.”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Chen et al. (2023)
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 1× · see citing works
“For example, AI is used in “profiling and prediction”, “assessment and evaluation”, “adaptive sys - tems and personalization”, and “intelligent tutoring systems” in teaching, administra - tive and institutional services in higher education (Zawacki-Richter et al., 2019 , p. 1).”
MethodDeep learning goes to school: toward a relational understanding of AI in education
Perrotta, C., & Selwyn, N. (2020) · Learning, Media and Technology · doi:10.1080/17439884.2020.1686017 · Cited by 299 · see citing works
The future of higher education in the light of artificial intelligence transformations
Aldosari, S. A. M. (2020) · International Journal of Higher Education · doi:10.5430/ijhe.v9n3p145 · Cited by 185 · see citing works
Howard, C., Jordan, P., Di Eugenio, B., & Katz, S. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0071-y · Cited by 51 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Liang, J., Stephens, J. M., & Brown, G. T. L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1522841 · Cited by 112 · see citing works
- Authors
- Chen, H., Wang, Y., Li, Y., Lee, Y., Petri, A., & Cha, T.
- ORCID
- Chen, Huan
- Venue
- Education and Information Technologies
- Published
- 2022-10-12
- Volume
- 28
- Issue
- 4
- Pages
- 4093-4108
- ISSN
- 1360-2357
- 1573-7608
- Cited by
- 6
- References
- 5
- Language
- en
- Licence
- Funders
- National Science Foundation (NSF IUSE #1935076)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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