Skip to main content
SpringerLink
Log in

NaMemo2: Facilitating Teacher-Student Interaction with Theory-Based Design and Student Autonomy Consideration

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Teacher-student interaction (TSI) is essential for learning efficiency and harmonious teacher-student interpersonal relationships. However, studies on TSI support tools often focus on teacher needs while neglecting student needs and autonomy. To enhance both lecturer competence in delivering interpersonal interaction and student autonomy in TSI, we developed NaMemo2, a novel augmented-reality system that allows students to express their willingness to TSI and displays student information to teachers during lectures. The design and evaluation process follows a new framework, STUDIER, which can facilitate the development of theory-based ethnics-aware TSI support tools in general. The quantitative results of our four-week field study with four classes in a university suggested that NaMemo2 can improve (1) TSI in the classroom from both teacher and student perspectives, (2) student attitudes and willingness to TSI, and (3) student attitudes to the deployment of NaMemo2. The qualitative feedback from students and teachers indicated that improving TSI may be responsible for improved attention in students and a better classroom atmosphere during lectures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Ahuja, K., Kim, D., Xhakaj, F., Varga, V., Xie, A., Zhang, S., & Agarwal, Y. (2019). EduSense: Practical Classroom Sensing at Scale. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 1–26. https://doi.org/10.1145/3351229.

  • Alavi, H. S., & Dillenbourg, P. (2012). An ambient awareness tool for supporting supervised collaborative problem solving. IEEE Transactions on Learning Technologies, 5(3), 264–274. https://doi.org/10.1109/TLT.2012.7.

    Article  Google Scholar 

  • Alavi, H. S., Dillenbourg, P., & Kaplan, F. (2009). Distributed awareness for class orchestration. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5794 LNCS, 211–225. https://doi.org/10.1007/978-3-642-04636-0_21.

  • An, P., Bakker, S., Ordanovski, S., Taconis, R., & Eggen, B. (2018). ClassBeacons: Designing distributed visualization of teachers’ physical proximity in the classroom. Proceedings of the Twelfth International Conference on Tangible Embedded and Embodied Interaction - TEI ’18, 357–367. https://doi.org/10.1145/3173225.3173243.

  • An, P., Bakker, S., Ordanovski, S., Taconis, R., Paffen, C. L. E., & Eggen, B. (2019). Unobtrusively enhancing reflection-in-action of teachers through spatially distributed ambient information. CHI 2019, 1–14. https://doi.org/10.1145/3290605.3300321.

  • An, P., Holstein, K., D’Anjou, B., Eggen, B., & Bakker, S. (2020). The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms. CHI 2020, 1–17. https://doi.org/10.1145/3313831.3376277.

  • Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning Media and Technology, 45(2), 115–128. https://doi.org/10.1080/17439884.2020.1686014.

    Article  Google Scholar 

  • Beyer, H., & Holtzblatt, K. (1998). Contextual design: Defining customer-centered systems. Morgan Kaufmann.

  • Brophy, J. (1999). Toward a model of the value aspects of motivation in education: Developing appreciation for particular learning domains and activities. Educational Psychologist, 34(2), 75–85. https://doi.org/10.1207/s15326985ep3402_1.

    Article  Google Scholar 

  • Brown, M., & Lowe, D. G. (2007). Automatic Panoramic Image Stitching Automatic 2D Stitching. International Journal of Computer Vision, 74(1), 59–73. Retrieved from https://doi.org/10.1007/s11263-006-0002-3.

  • Cooper, K. M., Haney, B., Krieg, A., & Brownell, S. E. (2017). What’s in a name? The importance of students perceiving that an instructor knows their names in a high-enrollment Biology Classroom. CBE—Life Sciences Education, 16(1), ar8. https://doi.org/10.1187/cbe.16-08-0265.

    Article  Google Scholar 

  • Cox, S. R., Wang, Y., Abdul, A., Weth, C., Von Der, & Lim, B. Y. (2021). Directed diversity: Leveraging language embedding distances for collective creativity in crowd ideation. CHI’21. https://doi.org/10.1145/3411764.3445782.

  • Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020). Retinaface: Single-shot multi-level face localisation in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 5202–5211. https://doi.org/10.1109/CVPR42600.2020.00525.

  • Doberstein, C., Charbonneau, É., Morin, G., & Despatie, S. (2022). Measuring the acceptability of facial recognition-enabled work Surveillance Cameras in the Public and Private Sector. Public Performance and Management Review, 45(1), 198–227. https://doi.org/10.1080/15309576.2021.1931374.

    Article  Google Scholar 

  • Donahue, A. K., & Miller, J. M. (2006). Experience, attitudes, and willingness to pay for public safety. American Review of Public Administration, 36(4), 395–418. https://doi.org/10.1177/0275074005285666.

    Article  Google Scholar 

  • Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30(3), 452–465. https://doi.org/10.1007/s12528-018-9179-z.

    Article  Google Scholar 

  • Eccles, J. S., & Roeser, R. W. (1999). School and community influences on human development. In M. H. Bornstein, & M. E. Lamb (Eds.), Developmental psychology: An advanced textbook (4th ed., pp. 503–554). L. Erlbaum.

  • Fagerland, M. W. (2012). t-tests, non-parametric tests, and large studies—a paradox of statistical practice? BMC Medical Research Methodology, 12(78), https://doi.org/10.1186/1471-2288-12-78.

  • Fernandez-Nieto, G., An, P., Zhao, J., Shum, B., S., & Martinez-Maldonado, R. (2022). Classroom Dandelions: Visualising Participant Position, Trajectory and Body Orientation Augments Teachers’ Sensemaking. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3491102.3517736.

  • Feuerstein, R., Rand, Y., Hoffman, M. B., & Vig, E. (1979). The dynamic assessment of retarded performers: the learning potential assessment device, theory, instruments, and techniques. 413.

  • Fogg, B. J. (2009). A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology - Persuasive ’09, 40:1–7. https://doi.org/10.1145/1541948.1541999.

  • Frich, J., Biskjaer, M. M., & Dalsgaard, P. (2018). Twenty years of creativity research in human-computer interaction: Current state and future directions. DIS 2018 - Proceedings of the 2018 Designing Interactive Systems Conference, 1235–1258. https://doi.org/10.1145/3196709.3196732.

  • Ghonsooly, B., & Hassanzadeh, T. (2019). Effect of interactionist dynamic assessment on English vocabulary learning: Cultural perspectives in focus. Issues in Educational Research, 29(1).

  • Glaser, B. G., & Strauss, A. L. (2006). The Discovery of grounded theory: strategies for qualitative research. AldineTransaction.

  • Glenz, T. (2014). The importance of learning students’ names. Journal on Best Teaching Practices, 1(1), 21–22.

    Google Scholar 

  • Hafen, C. A., Hamre, B. K., Allen, J. P., Bell, C. A., Gitomer, D. H., & Pianta, R. C. (2015). Teaching through interactions in secondary School Classrooms: Revisiting the factor structure and practical application of the Classroom Assessment Scoring system–secondary. Journal of Early Adolescence, 35(5–6), 651–680. https://doi.org/10.1177/0272431614537117.

    Article  Google Scholar 

  • Hagenauer, G., & Volet, S. E. (2014). Teacher-student relationship at university: An important yet under-researched field. Oxford Review of Education, 40(3), 370–388. https://doi.org/10.1080/03054985.2014.921613.

    Article  Google Scholar 

  • Hamre, B. K., Pianta, R. C., Downer, J. T., DeCoster, J., Mashburn, A. J., Jones, S. M., & Hamagami, A. (2013). Teaching through interactions: Testing a Developmental Framework ofTeacher Ef fectiveness in over 4,000 classrooms. The Elementary School Journal, 113(4), 461–487. https://doi.org/10.1086/669616.

    Article  Google Scholar 

  • Harper, B. (2018). Technology and teacher–student interactions: A review of empirical research. Journal of Research on Technology in Education, 50(3), 214–225. https://doi.org/10.1080/15391523.2018.1450690.

    Article  Google Scholar 

  • He, K., Gkioxari, G., Dollar, P., Girshick, R., & Mask (2017). R-CNN. ICCV 2017.

  • Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. International Conference on Artificial Intelligence in Education, 154–168. https://doi.org/10.1007/978-3-319-93843-1_12.

  • Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a Real-Time Classroom Orchestration Tool to support Teacher–AI Complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3.

    Article  Google Scholar 

  • Jiang, G., Shi, M., An, P., Su, Y., Wang, Y., & Lim, B. Y. (2020). NaMemo: Enhancing lecturers’ interpersonal competence of remembering students’ names. DIS 2020 Companion - Companion Publication of the 2020 ACM Designing Interactive Systems Conference. https://doi.org/10.1145/3393914.3395860.

  • Kelkar, S., Boushey, C. J., & Okos, M. (2015). A method to determine the density of foods using X-ray imaging. Journal of Food Engineering, 159, 36–41. https://doi.org/10.1016/j.jfoodeng.2015.03.012.

    Article  Google Scholar 

  • Korthagen, F. A. J., Attema-Noordewier, S., & Zwart, R. C. (2014). Teacher-student contact: Exploring a basic but complicated concept. Teaching and Teacher Education, 40, 22–32. https://doi.org/10.1016/j.tate.2014.01.006.

    Article  Google Scholar 

  • Kriegel, O. (2022). Encouraging Students to Participate: How to Help Shy Students Speak Up. Retrieved May 28, 2022, from https://www.wgu.edu/heyteach/article/encouraging-students-participate-how-help-shy-students-speak1809.html.

  • Li, L., & Yang, S. (2021). Exploring the influence of Teacher-Student Interaction on University Students’ Self-Efficacy in the flipped Classroom. Journal of Education and Learning, 10(2), 84. https://doi.org/10.5539/jel.v10n2p84.

    Article  Google Scholar 

  • Liebenberg, L., Theron, L., Sanders, J., Munford, R., van Rensburg, A., Rothmann, S., & Ungar, M. (2016). Bolstering resilience through teacher-student interaction: Lessons for school psychologists. School Psychology International, 37(2), 140–154. https://doi.org/10.1177/0143034315614689.

    Article  Google Scholar 

  • Long, J. (2021). Frameworks for HCI Research. In Approaches and Frameworks for HCI Research (pp. 13–22). https://doi.org/10.1017/9781108754972.004.

  • Luo, Y., Lee, B., Wohn, Y., Rebar, D., Conroy, A. L., D. E., & Choe, K. (2018). E. Time for Break: Understanding Information Workers’ Sedentary Behavior Through a Break Prompting System. CHI ’18: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3173574.3173701.

  • Malone, T. W., Laubacher, R., & Dellarocas, C. N. (2009). Harnessing crowds: Mapping the genome of collective intelligence. MIT Sloan Research Paper No, 4732–4709. https://doi.org/10.2139/ssrn.1381502.

  • Martinez-Maldonado, R., Mangaroska, K., Schulte, J., Elliott, D., Axisa, C., & Shum, S. B. (2020). Teacher tracking with integrity: What indoor positioning can reveal about instructional proxemics. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1–27. https://doi.org/10.1145/3381017.

  • Michie, S., van Stralen, M. M., & West, R. (2011a). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6(1), 42. https://doi.org/10.1186/1748-5908-6-42.

    Article  Google Scholar 

  • Michie, S., van Stralen, M. M., & West, R. (2011b). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6(1), 42. https://doi.org/10.1186/1748-5908-6-42.

    Article  Google Scholar 

  • Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007.

    Article  MathSciNet  Google Scholar 

  • Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014). The behavioral intervention technology model: An Integrated conceptual and Technological Framework for eHealth and mHealth Interventions. Journal of Medical Internet Research, 16(6), e146. https://doi.org/10.2196/jmir.3077.

    Article  Google Scholar 

  • Mummah, S. A., Robinson, T. N., King, A. C., Gardner, C. D., & Sutton, S. (2016). IDEAS (integrate, Design, assess, and share): A Framework and Toolkit of Strategies for the development of more Effective Digital Interventions to change Health Behavior. Journal of Medical Internet Research, 18(12), e317. https://doi.org/10.2196/jmir.5927.

    Article  Google Scholar 

  • Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological Reviews, 82(4), 591–605. https://doi.org/10.1111/j.1469-185X.2007.00027.x.

    Article  Google Scholar 

  • Nguyen, T. D., Cannata, M., & Miller, J. (2018). Understanding student behavioral engagement: Importance of student interaction with peers and teachers. Journal of Educational Research, 111(2), 163–174. https://doi.org/10.1080/00220671.2016.1220359.

    Article  Google Scholar 

  • Noble, D. (2022). Signs of a “Slow Learner” and Tips to Help Him/Her Excel. Retrieved from https://sg.theasianparent.com/slow-learners-tips-on-how-to-deal-with-a-slow-learner-child.

  • Oinas-Kukkonen, H. (2013). A foundation for the study of behavior change support systems. Personal and Ubiquitous Computing, 17(6), 1223–1235. https://doi.org/10.1007/s00779-012-0591-5.

    Article  Google Scholar 

  • Opdenakker, M. C., Maulana, R., & den Brok, P. (2012). Teacher–student interpersonal relationships and academic motivation within one school year: Developmental changes and linkage. School Effectiveness and School Improvement, 23(1), 95–119. https://doi.org/10.1080/09243453.2011.619198.

    Article  Google Scholar 

  • Pianta, R. C. (2016). Teacher–student interactions: Measurement, impacts, improvement, and policy. Policy Insights from the Behavioral and Brain Sciences, 3(1), 98–105. https://doi.org/10.1177/2372732215622457.

    Article  Google Scholar 

  • Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: Standardized observation can leverage capacity. Educational Researcher, 38(2), 109–119. https://doi.org/10.3102/0013189X09332374.

    Article  Google Scholar 

  • Poehner, M. E., & Wang, Z. (2021, October 1). Dynamic Assessment and second language development. Language Teaching, Vol. 54, pp. 472–490. https://doi.org/10.1017/S0261444820000555.

  • Pressley, M., Roehrig, A. D., Raphael, L., Dolezal, S., Bohn, C., Mohan, L., & Hogan, K. (2003). Teaching Processes in Elementary and Secondary Education. Handbook of Psychology, 153–175. https://doi.org/10.1002/0471264385.WEI0708.

  • Saini, M. K., & Goel, N. (2019). How smart are smart classrooms? A review of smart classroom technologies. ACM Computing Surveys, 52(6), https://doi.org/10.1145/3365757.

  • Saquib, N., Bose, A., George, D., & Kamvar, S. (2018). Sensei: Sensing Educational Interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4), 1–27.

  • SAS (2022). JMP. Retrieved from https://www.jmp.com/.

  • Schwarzer, R. (2008). Modeling Health Behavior Change: How to predict and modify the adoption and maintenance of Health Behaviors. Applied Psychology: An International Review, 57(1), 1–29. https://doi.org/10.1111/j.14640597.2007.00325.x.

    Article  Google Scholar 

  • Smith, M., & Miller, S. (2022). The ethical application of biometric facial recognition technology. AI and Society, 37(1), 167–175. https://doi.org/10.1007/s00146-021-01199-9.

    Article  Google Scholar 

  • Song, Q., Wang, Z., & Li, J. (2012). Residents’ behaviors, attitudes, and willingness to pay for recycling e-waste in Macau. Journal of Environmental Management, 106, 8–16. https://doi.org/10.1016/j.jenvman.2012.03.036.

    Article  Google Scholar 

  • Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/jgme-5-4-18.

  • Sun, H. L., Sun, T., Sha, F. Y., Gu, X. Y., Hou, X. R., Zhu, F. Y., & Fang, P. T. (2022). The influence of teacher–student Interaction on the Effects of Online Learning: Based on a serial Mediating Model. Frontiers in Psychology, 13(March), https://doi.org/10.3389/fpsyg.2022.779217.

  • Swanson, H. L., & Lussier, C. M. (2001). A selective synthesis of the experimental literature on dynamic Assessment. Review of Educational Research, 71(2), 321–363.

    Article  Google Scholar 

  • Tanner, K. D. (2011). Moving theory into practice: A reflection on teaching a large, introductory biology course for majors. CBE Life Sciences Education, 10(2), 113–122. https://doi.org/10.1187/cbe.11-03-0029.

    Article  Google Scholar 

  • Tanner, K. D. (2013). Structure matters: Twenty-one teaching strategies to promote student engagement and cultivate classroom equity. CBE Life Sciences Education, 12(3), 322–331. https://doi.org/10.1187/cbe.13-06-0115.

    Article  MathSciNet  Google Scholar 

  • Trauth, J. M., Jewell, I. K., Ricci, E., Musa, D., & Siminoff, L. (2000). Public attitudes regarding willingness to participate in medical research studies. Journal of Health and Social Policy, 12(2), 23–43. https://doi.org/10.1300/J045v12n02_02.

    Article  Google Scholar 

  • Vygotsky, L. S. (1978). Mind in Society. The Development of Higher Psychological Processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). https://doi.org/10.2307/1421493.

  • Wang, Y., & Reiterer, H. (2019). The point-of-choice prompt or the always-on progress bar?: A pilot study of reminders for prolonged sedentary behavior change. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3290607.3313050.

  • Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019a). Designing Theory-Driven user-centric explainable AI. CHi ’19. https://doi.org/10.1145/3290605.3300831.

  • Wang, Y., Fadhil, A., Lange, J. P., & Reiterer, H. (2019b). Integrating Taxonomies Into Theory-Based Digital Health Interventions for Behavior Change: A holistic Framework. JMIR Research Protocols, 8(1), e8055. https://doi.org/10.2196/resprot.8055.

    Article  Google Scholar 

  • Wang, Y., König, L. M., & Reiterer, H. (2021). A smartphone app to support sedentary behavior change by visualizing personal mobility patterns and Action Planning (SedVis): Development and Pilot Study. JMIR Formative Research, 5(1), e15369. https://doi.org/10.2196/15369.

    Article  Google Scholar 

  • Wang, Y., Venkatesh, P., & Lim, B. Y. (2022). Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation. CHI 2022. https://doi.org/10.1145/3491102.3517551.

  • Whitley, B. E. Jr., D. V. P., & Keith-Spiegel, D. W. B. P. (2000). A. F. W. Fairness in the Classroom. APS Observer. Retrieved from http://www.psychologicalscience.org/teaching/tips/tips_0700.html.

  • Wubbels, T., & Brekelmans, M. (2005). Two decades of research on teacher-student relationships in class. International Journal of Educational Research, 43(1–2), 6–24. https://doi.org/10.1016/j.ijer.2006.03.003.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Pengcheng An acknowledges funding support from Shenzhen Grant for Universities Stability Support Program (Grant Number: 2022081517130800).

Author information

Authors and Affiliations

  1. School of Telecommunications Engineering, Xidian University, Xi’an, China

    Guang Jiang, Jiahui Zhu & Yunsong Li

  2. School of Design, Southern University of Science and Technology, Shenzhen, China

    Pengcheng An

  3. Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis North Tower, 138632, Singapore, Singapore

    Yunlong Wang

Authors
  1. Guang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiahui Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunsong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengcheng An
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunlong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GJ, YL, PA, and YW conceptualized and designed the study. GJ and JZ developed the system and conducted the study. YW and JZ analyzed and interpreted the collected data. YW, GJ, and PA drafted the paper. All the authors reviewed, revised, and approved the submitted version.

Corresponding author

Correspondence to Yunlong Wang.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, G., Zhu, J., Li, Y. et al. NaMemo2: Facilitating Teacher-Student Interaction with Theory-Based Design and Student Autonomy Consideration. Educ Inf Technol 29, 7259–7279 (2024). https://doi.org/10.1007/s10639-023-12059-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-023-12059-y

Keywords

Search

Navigation