• Oka, M., & Okada, K. (2022). Scalable Bayesian approach for the DINA Q-matrix estimation combining stochastic optimization and variational inference. Psychometrika. https://link.springer.com/article/10.1007/s11336-022-09884-4
  (Its arXiv version is available at https://arxiv.org/abs/2105.09495)
• Oka, M., Saso, S., & Okada, K. (2022). Variational Bayesian inference for a polytomous-attribute saturated diagnostic classification model with parallel computing. Behaviormetrika. https://doi.org/10.1007/s41237-022-00164-0
  (Its arXiv version is available at https://arxiv.org/abs/2107.01865)

Papers Submitted

• Saso, S., Oka, M., & Uesaka, Y. (2022). Classifying the depth of understanding using cognitive diagnostic models and its application to a regular test: Examining the applicability of cognitive diagnostic models with a Q-matrix specified through a qualitative and quantitative procedure. Submitted to Cognitive Studies: Bulletin of the Japanese Cognitive Science Society. (in Japanese)
  （原文）佐宗駿・岡元紀・植阪友理 (2022). 認知診断モデルを活用した理解の深さの分類と定期テストへの応用：定性的・定量的なQ行列の設定とモデルの実践的有用性の検討. Submitted to 認知科学.

Preprints

• Oka, M., & Okada, K. (2021). Assessing the performance of diagnostic classification models in small sample contexts with different estimation methods. arXiv. https://arxiv.org/abs/2104.10975

Presentations

Invited Presentations

• Okada, K., Fukushima, K., & Oka, M. (2022). Diagnostic classification modeling for making the most use of item response data [Oral presentation]. Japanese Joint Statistical Meeting 2022 JSS-KSS-CSA Joint Session: Complex Data Analysis, Tokyo, Japan.
• Saso, S., Oka, M., Shiba, S., & Uesaka, Y. (2022). The practical application of cognitive diagnostic models to encourage students’ deep understanding and use of effective learning strategies [Oral presentation]. The 11th Conference of the Asian Regional Section of the International Association for Statistical Computing (IASC-ARS) 2022, Kyoto, Japan.

Referred Presentations

• Oka, M., Chen, Y., & Moustaki, I. (accepted). A doubly stochastic gradient algorithm for high-dimensional latent variable models [Oral presentation]. International Meeting of Psychometric Society (IMPS) 2023, Washington, D.C., USA.
• Hijikata, K., Oka, M., & Okada, K. (accepted). Sparse Bayesian joint modal estimation for item factor analysis [Oral presentation]. International Meeting of Psychometric Society (IMPS) 2023, Washington, D.C., USA.
• Saso, S., Oka, M., & Uesaka, Y. (2023). Development of assessment tools for depth of understanding quantitatively with cognitive diagnostic models [Poster presentation]. Future of Information and Communication Conference (FICC) 2023, Online.
• Hijikata, K., Oka, M., Yamaguchi, K., & Okada, K. (2022). The development of "variationalDCM" an R package performing variational Bayesian estimation for DCMs [Oral presentation]. The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics) 2022, London, United Kingdom.
• Saso, S.^†, Oka, M.^†, & Uesaka, Y. (2021). How can we statistically gauge students’ deep understanding from high school regular tests? [Poster presentation]. Junior Researchers of European Association for Research on Learning and Instruction (JURE) 2021, Online. (^†equal contribution) 
• Oka, M., & Okada, K. (2021). New iterative algorithm for the DINA Q-matrix estimation [Oral presentation]. International Meeting of Psychometric Society (IMPS) 2021, Online. 
• Oka, M., Saso, S., & Okada, K. (2021). Parallelized variational Bayesian algorithm for the polytomous-attribute saturated diagnostic classification model [Oral presentation]. World Meeting of the International Society for Bayesian Analysis (ISBA) 2021, Online. 

• Saso, S., Oka, M., & Uesaka, Y. (2022). Application of cognitive diagnostic models for quantitative assessment of depth of understanding and its proposed process: Q-matrix design utilizing the findings of psychology and mathematical education and Q-matrix estimation [Poster presentation]. The 39th Annual Meeting of Japanese Cognitive Science Society, Tokyo, Japan. (in Japanese)
  （原文）佐宗駿・岡元紀・植阪友理 (2022). 理解の深さの定量的評価を目指した認知診断モデルの応用とそのプロセスの提案–心理学・教科教育の知見とQ行列推定を活かしたQ行列作成– [ポスター発表]. 日本認知科学会第39回大会, 東京, 日本.

Non-Referred Presentations

IN JAPANESE

• Saso, S., Oka, M., Shiba, S., & Uesaka, Y. (2022). Quantitatively evaluating the degree of student's understanding and instructing learning strategies: Practical applications of cognitive diagnostic models and student's responses [Poster presentation]. The 20th conference of the Japan Association for Research on Testing, Online. (in Japanese)
• Oka, M., & Okada, K. (2021). Learning latent structures in Q-restricted latent class models with a non-compensatory assumption [Oral presentation]. Japanese Joint Statistical Meeting 2021 Competition Session, Online. (in Japanese)
• Saso, S., Oka, M., & Uesaka, Y. (2021). An empirical analysis of deep understanding through cognitive diagnostic models: Analysis using a large-scale standardized achievement test and its comparison with traditional perspectives [Oral presentation]. The 19th conference of the Japan Association for Research on Testing, Online. (in Japanese)
• Saso, S., Oka, M., & Uesaka, Y. (2021). Measuring deep understanding and its application to regular examinations: Applying cognitive diagnostic models to teaching practice and the responses from classroom teachers [Oral presentation]. Spring conference 2021 of the Japan Society of Educational Technology, Online. (in Japanese) 
• Oka, M., & Okada, K. (2020). Assessing the performance of Q-matrix estimation in a large-scale assessment [Oral presentation]. Young Researchers’ Seminar 2020 of the Japanese Society of Computational Statistics, Online. (in Japanese) 
• Oka, M., & Okada, K. (2020). Q matrix estimation algorithm using variational inference and stochastic optimization [Oral presentation]. The 48th annual meeting of the Behaviormetric Society, Tokyo, Japan. (Conference canceled) 
• Oka, M., & Okada, K. (2020). Q matrix estimation algorithm combined with variational inference and sampling technique [Oral presentation]. The 34th conference of the Japanese Society of Computational Statistics, Online. (in Japanese)