Having been an educator since 1992, I am interested in learning; specifically, how we learn, how we learn best, what conditions are optimal for learning, and what are the actual neurobiological processes associated with different aspects of learning. Below, you will see a poster that represents the preliminary work in this first study. Details are in the written description below.
Here is an outline of the research I am just beginning as a major component of my PhD work:
Differences in brain activity associated with learning via lecture vs. constructivist teaching method
Proposal Narrative
Rationale
The majority of classroom teaching at both high school and university is done via lecture style. Interestingly, it has been known for some time that teaching via a lecture format is ineffective and results in lower levels of knowledge retention relative to other, more engaging teaching methods. (e.g. Freeman et al, 2014; Deslauriers et al, 2019; Galia, Maria Lourdes, 2016; Zheng et al, 2023)
In general, findings in this area share results favouring active engagement in the learning process, as compared to a more learner-passive approach like lecturing. One method for ensuring such active engagement is to apply a constructivist approach, where the teaching style requires learners to construct new knowledge through a guided process involving the detection and analysis of patterns, the development of hypotheses, and the testing of those hypotheses. Through this experiential and engaging process, learners build their own representations of knowledge and incorporate new information into their existing schemas. Furthermore, because memories tend to be stronger when they include both episodic and not just semantic processes (Greenberg et al, 2010), teaching approaches that incorporate more learner experience and engagement offer an opportunity for the use of both kinds of memory formation.
Learning is about more than just the ability to recall facts, though. Lecture-style teaching, while effective for lower-order learning like recall and comprehension, does not provide a structure or a means to help students reach high-order learning, like application, synthesis, and evaluation of concepts (Anderson et al, 2001). It is these higher-order thinking skills that show something has truly been learned and not just remembered for a test. (Jensen, J.L. et al., 2014; Rayahu, A. et al., 2021).
In this study, behavioural demonstrations of learning as well as brain activity via EEG recording and analysis will be examined. The purpose of this two-pronged approach is to look beyond memory retention performance comparisons to see if there is, in fact, a significantly different way of thinking during the learning process and the recall process, as well as the demonstration of learning depth when tested, depending on which method is used in teaching a concept – lecture-style or constructivist-style.
Lecture-style teaching is relatively simple to represent in a study. What constitutes an engaging lesson, on the other hand, is not as well-standardized. For that reason, this study is explicit about what kind of teaching method is being tested in comparison to lecturing: a Concept Attainment method. (Bruner, 1977) Concept attainment is constructivist in nature, and, because it follows a specific structure regardless of the concept being taught, can help to remove a number of difficult-to-measure factors involved in other forms of engaging teaching styles (e.g. charisma and personality of the teacher, teacher experience, technology used)
Because of the many a varied difficulties associated with proving the efficacy of a teaching approach – validity of learning outcome measures, fidelity of the implementation of a promising teaching technique, long- versus short-term effects, and the social complexity of a classroom – research in this area is often so narrow that it is difficult for a teaching practitioner to know if a practice should be applied, and, if so, how to apply it in the field without the controls used in the research setting. For these reasons, this study is attempting to simplify but not sterilize the human activity of teaching and learning. That is to say, the Concept Attainment process used in this study is fully transferable to a real classroom setting but is controllable enough to be used in this research.
This study also combines an analysis of brain activity associated with two different teaching and learning practices, rather than just focusing on differences in retention performance. This combination is intended to explain how any differences that are found in recall and comprehension, as well as demonstrations of high-order thinking, may be related to what methods of teaching best contribute to how our brains learn best, thereby making the findings more applicable to more real-world, educational contexts.
The use of mobile EEG for research has been well-validated. (Krigolson et al., 2017), (Krigolson et al., 2021). Mobile EEG in this case makes good sense, as it will allow us to conduct lessons in a typical classroom environment, making the findings that much more valid and applicable as compared to studies confined to laboratory environments. Not only has mobile EEG been validated, but its use has also been encouraged for research just like this. Mobile EEG has been used in classroom environments previously and some best practices have emerged in terms of reducing noise in EEG signals in such stimulating environments (Xu et al., 2022), and how to analyze data in ways that can help pinpoint relevant activity (Fishburne et al., 2018).
Project Description
In the first study in Year One, fifty high school participants (aged 14 to 18) will be randomly selected from local high schools. Participants will be randomly assigned to one of two groups: Group A and Group B. Group A, the control group, will be taught a concept using a lecture-based method. The teacher for Group A will introduce a concept, explain it, define it, and give examples. Group B will be taught the same concept using the constructivist Concept Attainment method. The teacher for Group B will share “yes” and “no” examples of the concept being taught leaving learners to develop and test hypotheses about what they think the concept is, after which time the teacher will name the concept and confirm the correctness of the hypotheses created. While specific teaching practices incorporating constructivist approaches to learning can vary widely, this study will use Concept Attainment. Concept Attainment is used to ensure that the learning experience for Group B, while compact in terms of time, is indeed constructivist in nature while also structured enough so as to be able to be replicated faithfully. Both lessons A and B will take place in a typical high school classroom group setting.
Participants will each be fitted with a Muse-S (2nd generation) EEG headband prior to the lesson. Participants will wear the headbands throughout the entire lesson, each lasting approximately 45 minutes, and their EEG data will be recorded continuously.
While there are known challenges involved in retrieving clean data from mobile EEG used in non-laboratory settings, there is an emerging knowledge base on how to address these challenges, and an acceptance of the validity of such research when proper practices are followed at the collection stage as well as the analysis stage. (Krigolson et al., 2021; Fishburne et al., 2018; Xu et al., 2022)
Two weeks after the lesson, participants will be recalled for phase two of the study. This second stage will take place in a lab environment at the University of Victoria and participants will be tested individually and separately. Participants will be fitted with an X.on 7-channel EEG headset. Once fitted, they will first take part in standard cognitive testing (Cog Assess) to help establish personal EEG baselines. Then, they will complete a short quiz based on the concept taught in the lesson. This will also be done individually in a lab environment with EEG recording capturing the entire administration of the quiz. Finally, each participant will complete a short survey to help identify personal factors that may be considered significant in the analysis of the EEG data.
Standard EEG signal processing techniques will be used to analyze the continuous EEG data from the lesson phase (e.g. changes in brain oscillations over the course of the lesson) and the continuous EEG data from the retrieval phase (e.g., changes in brain oscillations over the course of the quiz and event-related potentials associated with certain discrete aspects of the quiz). Further, we will compare directly the differences in neural activity between the two teaching styles (lecture, constructivist).
Later in Year One, this entire process will be repeated with another group of 50 high school students. This is to better ensure replicability of the study and to increase the overall sample size.
In Year Two, in a second follow up study, we will repeat the method in its entirety, but this time in a university classroom environment and with undergraduate students as the participants. The first part of the study will involve 50 university undergraduates and the second part will repeat the process with 50 more university undergraduates, just as was done with the high school participants.
University students will be studied to see if the same patterns in brain activity are observed in slightly older participants. This takes into consideration potential differences in brain development, differences in learning context, and differences in difficulty of concepts being taught.
Procedures for Data Analysis
Recording of mobile EEG data from the classroom activity stage of each part of the study will be done using Muse-S (2nd generation) connected via Bluetooth to a tablet. EEG data for the quiz stage of each part of the study will be done in the laboratory using the X.on EEG headset. While the classroom task is too complex for the detection of Event-Related Potentials (ERPs), the Concept Attainment process will be facilitated in such a way as to be segmented into discrete sections, some of which involve talking and listening with peers, and some involve no talking or listening but only solitary thought and attention. This will offer an opportunity to create markers (manually) that correspond with specific common stages in each segment and, thereby, to compare data that corresponds to more specific processes and behaviours within the overall lesson.
Raw continuous EEG data from the classroom task will be filtered for artifacts following a common acceptance/rejection protocol. Then, EEG data will undergo a Fast Fourier Transform to convert waveform data in the time domain into the frequency domain. The FFT results will be standardized and then averaged for each participant, allowing for the comparison of waveforms from participant to participant.
Raw continuous data as well as ERP data from each participant will be collected during the laboratory-based quiz portion of the study. Procedures for the comparison of waveforms in the raw EEG data will be the same as those used in the classroom part of the study. ERP data will be collected in synchronization with a MATLAB-facilitated quiz, allowing us to see the ERPs that are evoked in response to specific recall prompts. ERPs among individuals in each of the two groups will be averaged, creating a grand average for each group (i.e. lecture (control) and Concept Attainment) allowing for a comparison of brain activity between the two groups.
Performance data collected in the quiz stage will be analyzed separately. The quiz items, designed to measure performance at different depth of learning – recall, comprehension, application, synthesis, and evaluation – will be piloted before use to verify item difficulty and discrimination using point-biserial correlation. With difficulty of each items known, the responses to quiz items by participants can then be compared using individual t-tests.
Dissemination of Findings
Findings in this study will be shared with a variety of audiences and for a variety of reasons. The study will be shared formally in the form of a series of published journal articles, allowing educational researchers as well as Neuroscience researchers to have access. Beyond the academic world, findings will be shared with educational practitioners and leaders in education jurisdictions. This will be done in writing, as well as via podcast, presentations at education conferences, and meeting directly with members of interested education communities. It is hoped that the findings will be of significance and that they will help inform no only further research, but also future practice in teaching settings at both the high school and university levels.
(1895 words)
References (so far)
Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., Raths, J., & Wittrock, M. C. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: Longman.
Bruner, Jerome. (1977). The Process of Education: Revised Edition. Harvard University Press.
Deslauriers, Louis & McCarty, Logan & Miller, Kelly & Callaghan, Kristina & Kestin, Greg. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences. 116 (39), 19251–19257. https://doi.org/10.1073/pnas.1821936116
Fishburn, Frank A, Murty, V, Hlutkowsky, Christina, MacGillivray, Caroline, Bemis, Lisa, Murphy, Meghan E, Huppert, Theodore J, Perlman, Susan B. Putting our heads together: interpersonal neural synchronization as a biological mechanism for shared intentionality, Social Cognitive and Affective Neuroscience, Volume 13, Issue 8, August 2018, Pages 841–849. https://doi.org/10.1093/scan/nsy060
Freeman, S., Eddy, S., McDonough, M., Smith, M., Okoroafor, N., Jordt, H., Wenderoth, M. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
Galia, Maria Lourdes. (2016). Constructivist-Based Approach in Teaching Mathematics: A Quasi-Experimental Study. Research Journal of Educational Sciences, Vol. 4 (10), 1-4.
Greenberg, D. L., Verfaellie, M. (2010). Interdependence of episodic and semantic memory: Evidence from neuropsychology. Journal of the International Neuropsychological Society, 16 (5), 748–753.
Jensen, J. L., McDaniel, M. A., Woodard, S. M., & Kummer, T. A. (2014). Teaching to the Test…or Testing to Teach: Exams Requiring Higher Order Thinking Skills Encourage Greater Conceptual Understanding. Educational Psychology Review, 26(2), 307–329.
Krigolson, O. E., Williams, C. C., Norton, A., Hassall, C. D., Colino, F. L. (2017). Choosing MUSE: Validation of a Low-Cost Portable EEG System for ERP Research. Frontiers in Neuroscience: Brain Imaging Methods 11(109), 1-10. https://doi.org/10.3389/fnins.2017.00109
Krigolson, O.E., Hammerstrom, M.R., Abimbola, W., Trska R., Wright, B.W., Hecker, K.G., Binsted, G. (2021). Using Muse: Rapid Mobile Assessment of Brain Performance. Frontiers of Neuroscience 15. https://doi.org/10.3389/fnins.2021.634147
Rahayu, A., Syah, A., & Najib, A. (2021, June). Higher order thinking skills students in mathematical statistics course base on revised bloom taxonomy in factual and conceptual knowledge dimension. In Journal of Physics: Conference Series (Vol. 1918, No. 4, p. 042076). IOP Publishing.
Xu, K., Torgrimson, S.J., Torres, R., Lenartowicz, A., Grammer, J.K. (2022). EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children. Mind, Brain, and Education, 16: 221-227. https://doi.org/10.1111/mbe.12314 Zheng, Q.-M., Li, Y.-Y., Yin, Q., Zhang, N., Wang, Y.-P., Li, G.-X., & Sun, Z.-G. (2023). The effectiveness of problem-based learning compared with lecture-based learning in Surgical Education: A systematic review and meta-analysis. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04531-7