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Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts (2403.03920v1)

Published 6 Mar 2024 in cs.AI, cs.CL, and cs.HC

Abstract: This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts. We integrate Richard Elmore's Instructional Core Framework to examine how AI and ML methods, particularly NLP, can analyze educational content, teacher discourse, and student responses to foster instructional improvement. Through a comprehensive review and case studies within the Instructional Core Framework, we identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development. We unveil patterns that indicate AI/ML not only streamlines administrative tasks but also introduces novel pathways for personalized learning, providing actionable feedback for educators and contributing to a richer understanding of instructional dynamics. This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings, advocating for a balanced approach that considers ethical considerations, data quality, and the integration of human expertise.

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References (78)
  1. Dor Abrahamson and Raúl Sánchez-García. 2016. Learning Is Moving in New Ways: The Ecological Dynamics of Mathematics Education. Journal of the Learning Sciences 25, 2 (April 2016), 203–239. https://doi.org/10.1080/10508406.2016.1143370 Publisher: Routledge _eprint: https://doi.org/10.1080/10508406.2016.1143370.
  2. Ashraf Alam. 2023. Harnessing the Power of AI to Create Intelligent Tutoring Systems for Enhanced Classroom Experience and Improved Learning Outcomes. In Intelligent Communication Technologies and Virtual Mobile Networks (Lecture Notes on Data Engineering and Communications Technologies), G. Rajakumar, Ke-Lin Du, and Álvaro Rocha (Eds.). Springer Nature, Singapore, 571–591. https://doi.org/10.1007/978-981-99-1767-9_42
  3. Robin Alexander. 2008. Culture, dialogue and learning: Notes on an emerging pedagogy. Exploring talk in school 2008 (2008), 91–114. https://www.torrossa.com/gs/resourceProxy?an=4911977&publisher=FZ7200#page=110
  4. A. Bain and G. Swan. 2011. Technology enhanced feedback tools as a knowledge management mechanism for supporting professional growth and school reform. Educational Technology Research and Development 59 (2011), 673–685. https://doi.org/10.1007/S11423-011-9201-X
  5. Educational Data Mining and Learning Analytics: Applications to Constructionist Research. Technology, Knowledge and Learning 19 (July 2014). https://doi.org/10.1007/s10758-014-9223-7
  6. Ali Borji. 2023. A Categorical Archive of ChatGPT Failures. https://doi.org/10.48550/arXiv.2302.03494 arXiv:2302.03494 [cs].
  7. Leveraging ChatGPT to Democratize and Decolonize Global Surgery: Large Language Models for Small Healthcare Budgets. World Journal of Surgery 47, 11 (Nov. 2023), 2626–2627. https://doi.org/10.1007/s00268-023-07167-2
  8. Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. (2023). https://policycommons.net/artifacts/3854312/ai-report/4660267/
  9. Extracting Training Data from Large Language Models. 2633–2650. https://www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting
  10. Huanyi Chen. 2018. Predicting Student Performance Using Data from an Auto-Grading System. Master’s thesis. University of Waterloo. https://uwspace.uwaterloo.ca/handle/10012/13435 Accepted: 2018-06-25T18:49:07Z.
  11. Gobinda G. Chowdhury. 2003. Natural Language Processing. Annual Review of Information Science and Technology (ARIST) 37 (2003), 51–89. ERIC Number: EJ659664.
  12. Instructional rounds in education. Vol. 30. Cambridge, MA: Harvard Education Press. https://www.education.ne.gov/wp-content/uploads/2021/11/Instructional-Rounds-in-Education-Elmores-Instructional-Core.pdf
  13. Improving Automated Evaluation of Student Text Responses Using GPT-3.5 for Text Data Augmentation. In Artificial Intelligence in Education (Lecture Notes in Computer Science), Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, and Vania Dimitrova (Eds.). Springer Nature Switzerland, Cham, 217–228. https://doi.org/10.1007/978-3-031-36272-9_18
  14. Chapter 37 - Intelligent Tutoring Systems. In Handbook of Human-Computer Interaction (Second Edition), Marting G. Helander, Thomas K. Landauer, and Prasad V. Prabhu (Eds.). North-Holland, Amsterdam, 849–874. https://doi.org/10.1016/B978-044481862-1.50103-5
  15. Charlotte Danielson. 2013. EVALUATION INSTRUMENT. (2013).
  16. Dorottya Demszky and Heather Hill. 2023. The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts. https://doi.org/10.48550/arXiv.2211.11772 arXiv:2211.11772 [cs].
  17. Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial in a Large-Scale Online Course. Educational Evaluation and Policy Analysis (May 2023), 01623737231169270. https://doi.org/10.3102/01623737231169270 Publisher: American Educational Research Association.
  18. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://doi.org/10.48550/arXiv.1810.04805 arXiv:1810.04805 [cs].
  19. Do Components of Explicit Instruction Explain the Differential Effectiveness of a Core Mathematics Program for Kindergarten Students With Mathematics Difficulties? A Mediated Moderation Analysis. Assessment for Effective Intervention 44, 3 (June 2019), 197–211. https://doi.org/10.1177/1534508418758364 Publisher: SAGE Publications Inc.
  20. Richard Elmore. 2008. Improving the instructional core. Draft manuscript (2008). https://achievethecore.org/content/upload/Improving%20The%20Instructional%20Core_Elmore%20Article.pdf
  21. Richard Elmore. 2010. Leading the instructional core. Conversation, 11 (3) (2010), 1–12.
  22. Robyn M. Gillies. 2015. Enhancing Classroom-based Talk: Blending practice, research and theory. Routledge. Google-Books-ID: McQ0CwAAQBAJ.
  23. Roberto Gozalo-Brizuela and Eduardo C. Garrido-Merchan. 2023. ChatGPT is not all you need. A State of the Art Review of large Generative AI models. https://doi.org/10.48550/arXiv.2301.04655 arXiv:2301.04655 [cs].
  24. J. Hardman. 2016. Opening-up Classroom Discourse to Promote and Enhance Active, Collaborative and Cognitively-Engaging Student Learning Experiences. (2016). https://doi.org/10.14705/rpnet.2016.000400
  25. Impact of project-based curriculum materials on student learning in science: Results of a randomized controlled trial. Journal of Research in Science Teaching 52, 10 (2015), 1362–1385. https://doi.org/10.1002/tea.21263 _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/tea.21263.
  26. An analysis of the forms of teacher-student dialogue that are most productive for learning. Language and Education 37, 2 (March 2023), 186–211. https://doi.org/10.1080/09500782.2021.1956943
  27. Training Compute-Optimal Large Language Models. https://doi.org/10.48550/arXiv.2203.15556 arXiv:2203.15556 [cs].
  28. Promoting rich discussions in mathematics classrooms: Using personalized, automated feedback to support reflection and instructional change. Teaching and Teacher Education 112 (2022), 103631. https://www.sciencedirect.com/science/article/pii/S0742051X22000026 Publisher: Elsevier.
  29. Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376418
  30. Jaeho Jeon and Seongyong Lee. 2023. Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies 28, 12 (Dec. 2023), 15873–15892. https://doi.org/10.1007/s10639-023-11834-1
  31. Inspiring dialogue: Talking to learn in the English classroom. Teachers College Press. https://books.google.com/books?hl=en&lr=&id=yqdDAwAAQBAJ&oi=fnd&pg=PR7&dq=Juzwik+et+al.,+2013+classroom&ots=NBZk7y27MS&sig=0FvRibIh0Sf2oeeOEywS879rWb8
  32. T. Kakkonen and E. Sutinen. 2004. Automatic assessment of the content of essays based on course materials. In ITRE 2004. 2nd International Conference Information Technology: Research and Education. IEEE, London, England, UK, 126–130. https://doi.org/10.1109/ITRE.2004.1393660
  33. Using global observation protocols to inform research on teaching effectiveness and school improvement: Strengths and emerging limitations. Education Policy Analysis Archives 28 (April 2020), 62–62. https://doi.org/10.14507/epaa.28.5012
  34. Automatically Measuring Question Authenticity in Real-World Classrooms. Educational Researcher 47, 7 (Oct. 2018), 451–464. https://doi.org/10.3102/0013189X18785613 Publisher: American Educational Research Association.
  35. Ehsan Latif and Xiaoming Zhai. 2023. Fine-tuning ChatGPT for Automatic Scoring. http://arxiv.org/abs/2310.10072 arXiv:2310.10072 [cs].
  36. Jing Liu and Julie Cohen. 2021. Measuring Teaching Practices at Scale: A Novel Application of Text-as-Data Methods. Educational Evaluation and Policy Analysis 43, 4 (Dec. 2021), 587–614. https://doi.org/10.3102/01623737211009267 Publisher: American Educational Research Association.
  37. RoBERTa: A Robustly Optimized BERT Pretraining Approach. https://doi.org/10.48550/arXiv.1907.11692 arXiv:1907.11692 [cs].
  38. Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks. AERA Open 6, 3 (July 2020), 233285842094031. https://doi.org/10.1177/2332858420940312
  39. Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Processing Magazine 38, 3 (May 2021), 37–50. https://doi.org/10.1109/MSP.2021.3055032
  40. Naomichi Makinae. 2019. The Origin and Development of Lesson Study in Japan. In Theory and Practice of Lesson Study in Mathematics: An International Perspective, Rongjin Huang, Akihiko Takahashi, and João Pedro da Ponte (Eds.). Springer International Publishing, Cham, 169–181. https://doi.org/10.1007/978-3-030-04031-4_9
  41. The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Kalina Bontcheva and Jingbo Zhu (Eds.). Association for Computational Linguistics, Baltimore, Maryland, 55–60. https://doi.org/10.3115/v1/P14-5010
  42. Full Personalized Learning Path Recommendation: A Literature Review. In International Conference on Advanced Intelligent Systems and Informatics. Springer, 185–195.
  43. Classroom observation for evaluating and improving teaching: An international perspective. Studies in Educational Evaluation 49 (2016), 15–29. https://www.sciencedirect.com/science/article/pii/S0191491X15300389?casa_token=-EeNa0Imb78AAAAA:Fpx63O_R4rMzlGPjn6Fm1gL9ZL8fl-lTvvOQFdBF6e9MCQ-TY9f8m8DaW27tXXfqrDVRd1zqgvQ7
  44. Deliberative Discourse Idealized and Realized: Accountable Talk in the Classroom and in Civic Life. Studies in Philosophy and Education 27, 4 (July 2008), 283–297. https://doi.org/10.1007/s11217-007-9071-1
  45. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments 29, 1 (Jan. 2021), 142–163. https://doi.org/10.1080/10494820.2018.1558257 Publisher: Routledge _eprint: https://doi.org/10.1080/10494820.2018.1558257.
  46. Authentic Intellectual Work and Standardized Tests: Conflict or Coexistence? Improving Chicago’s Schools. (2001). Publisher: ERIC.
  47. Hyacinth S. Nwana. 1990. Intelligent tutoring systems: an overview. Artificial Intelligence Review 4, 4 (Dec. 1990), 251–277. https://doi.org/10.1007/BF00168958
  48. Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments 6, 1 (Sept. 2019), 9. https://doi.org/10.1186/s40561-019-0089-y
  49. Tony Read. 2015. Where Have All the Textbooks Gone?: Toward Sustainable Provision of Teaching and Learning Materials in Sub-Saharan Africa. World Bank Publications. Google-Books-ID: CwQ7CgAAQBAJ.
  50. Thomas Richter and Maggie McPherson. 2012. Open educational resources: education for the world? Distance Education 33, 2 (Aug. 2012), 201–219. https://doi.org/10.1080/01587919.2012.692068
  51. Pati Ruiz and Judi Fusco. 2023. Glossary of Artificial Intelligence Terms for EducatorsEducator CIRCLS Blog. Retrieved from Glossary of Artificial Intelligence Terms for Educators–CIRCLS (2023).
  52. Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design Matrix. Sustainability 15, 15 (Jan. 2023), 11524. https://doi.org/10.3390/su151511524 Number: 15 Publisher: Multidisciplinary Digital Publishing Institute.
  53. Eisuke Saito. 2012. Key issues of lesson study in Japan and the United States: a literature review. Professional Development in Education 38, 5 (Nov. 2012), 777–789. https://doi.org/10.1080/19415257.2012.668857
  54. Jay Paredes Scribner and Joe F. Donaldson. 2001. The Dynamics of Group Learning in a Cohort: From Nonlearning to Transformative Learning. Educational Administration Quarterly 37, 5 (Dec. 2001), 605–636. https://doi.org/10.1177/00131610121969442 Publisher: SAGE Publications Inc.
  55. A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access 10 (2022), 56720–56739.
  56. What the Discourse Tells Us: Talk and Indicators of High-Level Comprehension. International Journal of Educational Research 47 (2008), 372–391. https://doi.org/10.1016/J.IJER.2009.01.001
  57. Two Practical Rhetorical Structure Theory Parsers. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, Matt Gerber, Catherine Havasi, and Finley Lacatusu (Eds.). Association for Computational Linguistics, Denver, Colorado, 1–5. https://doi.org/10.3115/v1/N15-3001
  58. The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves. https://doi.org/10.48550/arXiv.2204.09652 arXiv:2204.09652 [cs].
  59. Galactica: A Large Language Model for Science. https://doi.org/10.48550/arXiv.2211.09085 arXiv:2211.09085 [cs, stat].
  60. Citizenship and Education in Twenty-Eight Countries: Civic Knowledge and Engagement at Age Fourteen. Technical Report. IEA Secretariat, Herengracht 487, 1017 BT, Amsterdam, The Netherlands. https://eric.ed.gov/?id=ED452116 ISBN: 9789051668346 ERIC Number: ED452116.
  61. Paul Tosey and Jane Mathison. 2010. Neuro‐linguistic programming as an innovation in education and teaching. Innovations in Education and Teaching International 47, 3 (Aug. 2010), 317–326. https://doi.org/10.1080/14703297.2010.498183 Publisher: Routledge _eprint: https://doi.org/10.1080/14703297.2010.498183.
  62. Artificial Intelligence in K-12 Education: eliciting and reflecting on Swedish teachers’ understanding of AI and its implications for teaching & learning. Education and Information Technologies (2023), 1–21.
  63. Machine Learning Model Sizes and the Parameter Gap. https://doi.org/10.48550/arXiv.2207.02852 arXiv:2207.02852 [cs].
  64. Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning. https://doi.org/10.48550/arXiv.2211.04325 arXiv:2211.04325 [cs].
  65. Candace A. Walkington. 2013. Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology 105, 4 (2013), 932–945. https://doi.org/10.1037/a0031882
  66. Ning Wang and James Lester. 2023. K-12 Education in the Age of AI: A Call to Action for K-12 AI Literacy. International journal of artificial intelligence in education 33, 2 (2023), 228–232.
  67. Rose E. Wang and Dorottya Demszky. 2023. Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction. https://doi.org/10.48550/arXiv.2306.03090 arXiv:2306.03090 [cs].
  68. PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization. https://doi.org/10.48550/arXiv.2306.05087 arXiv:2306.05087 [cs].
  69. Intelligent Auto-grading System. In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). 430–435. https://doi.org/10.1109/CCIS.2018.8691244
  70. Automatic classification of activities in classroom discourse. Computers & Education 78 (Sept. 2014), 115–123. https://doi.org/10.1016/j.compedu.2014.05.010
  71. Emergent Abilities of Large Language Models. https://doi.org/10.48550/arXiv.2206.07682 arXiv:2206.07682 [cs].
  72. Ethical and social risks of harm from Language Models. https://doi.org/10.48550/arXiv.2112.04359 arXiv:2112.04359 [cs].
  73. Classification of open-ended responses to a research-based assessment using natural language processing. Physical Review Physics Education Research 18, 1 (June 2022), 010141. https://doi.org/10.1103/PhysRevPhysEducRes.18.010141 Publisher: American Physical Society.
  74. Can Short Answers to Open Response Questions Be Auto-Graded Without a Grading Rubric? In Artificial Intelligence in Education, Elisabeth André, Ryan Baker, Xiangen Hu, Ma. Mercedes T. Rodrigo, and Benedict Du Boulay (Eds.). Vol. 10331. Springer International Publishing, Cham, 594–597. https://doi.org/10.1007/978-3-319-61425-0_72 Series Title: Lecture Notes in Computer Science.
  75. GPT Can Solve Mathematical Problems Without a Calculator. https://doi.org/10.48550/arXiv.2309.03241 arXiv:2309.03241 [cs].
  76. One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era. https://doi.org/10.48550/arXiv.2304.06488 arXiv:2304.06488 [cs].
  77. A Memory-Augmented Neural Model for Automated Grading. 189–192. https://doi.org/10.1145/3051457.3053982
  78. Red teaming chatgpt via jailbreaking: Bias, robustness, reliability and toxicity. arXiv preprint arXiv:2301.12867 (2023), 12–2. Publisher: Technical Report.
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Authors (5)
  1. Zewei Tian (2 papers)
  2. Min Sun (108 papers)
  3. Alex Liu (19 papers)
  4. Shawon Sarkar (5 papers)
  5. Jing Liu (526 papers)
Citations (2)
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