Teaching Evaluations

Lectures play an important role in facilitating student's learning process through knowledge transmission.  Consequently, from student’s perspectives certain lecturers’ characteristics can ensure the effectiveness of teaching practices and enhance the learning process. Specifically, the research and teaching experience of a lecturer as well as the prior acquisition of a diverse set of skills drawn from participation to teaching pedagogy workshops, team building, and leadership activities are extremely valuable for creating and fostering a positive learning environment.

Second, lectures are instrumental not only for the communication of conceptual knowledge but also for encouraging the discussion of ideas and concepts, motivating students' self-efficacy in learning, directing focus as well as emphasizing key learning objectives. Thus, encouraging students to attend lectures and write their own notes during the delivery of teaching sessions helps students to improve their understanding and further contribute to their learning process (expectations of teaching and learning). Overall, the use of effective and innovative teaching practices can maximize learning outcomes using research-based findings. In particular, an underline mechanism associated with learning is memory retention which can be optimised by understanding the role of content's variability and the intervals between learning sessions. A recent study present findings which reveal that spaced repetition enhances memory for identical information over long intervals, whereas variability in content improves recall of isolated features. Therefore, lecturers with a good understanding of such psychological and behavioral mechanisms have the ability of exploiting the presence of heterogeneity ability groups in reinforcing the effectiveness of learning processes and related pedagogical practices. 

Related Literature:

• Brady, M. P. (2013). "Multiple roles of student and instructor in university teaching and learning processes". The International Journal of Management Education, 11(2), 93-106. 

• Bui, D. C., & Myerson, J. (2014). "The role of working memory abilities in lecture note-taking". Learning and Individual Differences, 33, 12-22. 

• Bueno, D. (2019). "Genetics and learning: How the genes influence educational attainment". Frontiers in Psychology, 10, 454118. 

• Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D. J., & Long, Q. (2018). "Predicting academic performance by considering student heterogeneity". Knowledge-Based Systems, 161, 134-146. 

• Chew, S. L., & Cerbin, W. J. (2021). "The cognitive challenges of effective teaching". The Journal of Economic Education, 52(1), 17-40. 

• Konaszewski, K., Kolemba, M., & Niesiobędzka, M. (2021). "Resilience, sense of coherence and self-efficacy as predictors of stress coping style among university students". Current Psychology, 40(8), 4052-4062. 

• Simkins, Scott P., Mark H. Maier, and Phil Ruder. "Team-based learning (TBL): Putting learning sciences research to work in the economics classroom". The Journal of Economic Education 52.3 (2021): 231-240. 

• Mueller, F. A., & Wulf, T. (2022). "Blended learning environments and learning outcomes: The mediating role of flow experience". The International Journal of Management Education, 20(3), 100694. 

• Pi, Z., Zhang, Y., Yu, Q., & Yang, J. (2023). "A familiar peer improves students’ behavior patterns, attention, and performance when learning from video lectures". International Journal of Educational Technology in Higher Education, 20(1), 47. 

• Gan, W., Qi, Z., Wu, J., & Lin, J. C. W. (2023). "Large language models in education: Vision and opportunities". In 2023 IEEE International Conference on Big Data (BigData) (pp. 4776-4785). IEEE. 

• Tomlinson, A., Simpson, A., & Killingback, C. (2023). "Student expectations of teaching and learning when starting university: a systematic review". Journal of Further and Higher Education, 47(8), 1054-1073. 

• Weissman, D. L., Elliot, A. J., & Sommet, N. (2022). "Dispositional predictors of perceived academic competitiveness: Evidence from multiple countries". Personality and Individual Differences, 198, 111801. 

Motivation Methods

• Encouraging students to be active participants in learning and understanding.

• Providing timely feedback and constructive comments for further improvements on both formative and summative assessments. Overall good feedback has to follow the following principles: 

(i) be constructive; feedback should not just be about benchmarking students against the assessment criteria.  Good feedback should also always be feedforward, identifying both the strengths and the weakness of the work and make clear what students can do to improve.    

(ii) be specific; good feedback is specific to the students, clearly linked to the assessment task, the marking criteria and the ILOs for the module.

(iii) be consistent; good feedback is consistent across the whole module, ensuring individual markers are giving similar types of feedback and levels of detail to all students. 

• Encouraging class participation and attendance with weekly reminders on the main topics to be covered during lecturers (e.g., via Blackboard) or summarizing the main discussions and key learning objectives that were covered in a given week.

Overall, a high-quality mentoring pedagogy which supports the effective mentoring of undergraduate and postgraduate students consists of practices such as: (i) dedicating time to one-to-one meetings and mentoring to provide timely advice on academic matters, (ii) building community among groups of students (e.g., postgraduate taught students) or a research team, (iii) teaching technical skills, methods, and techniques when conducting research, (iv) supporting students' professional development, among other.   

Related Literature:

• Bearman, M., et al. (2024). "Enhancing Feedback Practices within PhD supervision: a qualitative framework synthesis of the literature". Assessment & Evaluation in Higher Education, 1-17. 

• Chugh, R., Macht, S., and Harreveld, B. (2022). "Supervisory feedback to postgraduate research students: a literature review". Assessment & Evaluation in Higher Education, 47(5), 683-697. 

Cognitive Diversity

This component is indeed crucial for enhancing inclusion in learning and helping students to achieve their potentials. However, the tools and methods commonly used to ensure that cognitive diversity is applied (such as the type of questions in assessments etc), should not be interfering with the way a module is designed.

Related Literature:

• Brinko, K. T. (1993). "The practice of giving feedback to improve teaching: What is effective?". The Journal of Higher Education, 64(5), 574-593. 

• Shinn, E., and Ofiesh, N. S. (2012). "Cognitive diversity and the design of classroom tests for all learners". Journal of Postsecondary Education and Disability, 25(3), 227-245. 

• He, M., and Liu, H. (2020). "The Application of Case Teaching Method in Econometrics Teaching". In 5th International Symposium on Social Science (ISSS 2019) (pp. 258-263). Atlantis Press. 

• Cladera, M. (2021). "Assessing the attitudes of economics students towards econometrics". International Review of Economics Education, 37, 100216. 

• Erden, S. (2023). "Enhancing learning outcomes in econometrics: a 12-year study". Education Sciences, 13(9), 913. 

Main Challenges in Teaching Econometrics & Statistics

One of the main challenges when teaching quantitative modules (such as econometrics, statistics, financial econometrics, applied econometrics etc.) at the university level, is the presence of a heterogeneous student population with diverse technical background. Under these circumstances to ensure high-quality teaching and learning environment in higher education settings it is important to employ engaged learning practices in order to bridge the gap in the knowledge of students. Some specific examples include the effective teaching of estimation methods for model parameters which includes approaches such as maximum likelihood estimation, GMM, lasso shrinkage that involve different technical difficulties both from the theoretic as well as the computational perspective. 

Another example is the challenge in ensuring the normalization of students' understanding in important notions and approaches commonly used in statistical decision theory (i.e., in econometric inference); especially with respect to the comparison of statistical approaches and empirical results obtained from methods based on different assumptions and conditions. For example, if we are interested to assess whether the specification of the conditional prior distribution is driving posterior inference in practice it requires to assess whether changes in this prior change the posterior. In other words, the informativeness of the prior distribution can only be assessed by evaluating the sensitivity of the posterior distribution to the choice of the prior rather by comparing prior and posterior distributions.

Related Literature:

• Angrist, J. D., and Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press. 

• Hendry, D. F., and Mizon, G. E. (2016). "Improving the Teaching of Econometrics". Cogent Economics & Finance, 4(1), 1170096. 

• Lee, C. F. (2020). "Financial econometrics, mathematics, statistics, and financial technology: an overall view". Review of Quantitative Finance and Accounting, 54(4), 1529-1578.

• Liru, B., Yiming, L., and Zhen, L. (2021). "An empirical study on the teaching effect of blended learning mode in financial econometrics course based on Logit Model". In 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE) (pp. 305-308). IEEE. 

• Giacomini, R., Kitagawa, T., and Read, M. (2022). "Narrative Restrictions and Proxies: Rejoinder". Journal of Business & Economic Statistics, 40(4), 1438-1441. 

How training the brain could boost economic growth

Related Literature:

• Berger, E. M., Fehr, E., Hermes, H., Schunk, D., and Winkel, K. (2025). "The Impact of Working-Memory Training on Children’s Cognitive and Noncognitive Skills". Journal of Political Economy, 133(2), 492-521. 

• Alan, S., and Mumcu, I. (2024). "Nurturing Childhood Curiosity to Enhance Learning: Evidence from a Randomized Pedagogical Intervention". American Economic Review, 114(4), 1173-1210. 

• Bartoli, E. et al. (2024). "Default mode network electrophysiological dynamics and causal role in creative thinking". Brain. 

• Matranga, A. (2024). "The ant and the grasshopper: Seasonality and the invention of agriculture". Quarterly Journal of Economics.  

• Wooten, T. (2024). "Effort Traps: Socially Structured Striving and the Reproduction of Disadvantage". American Journal of Sociology (forthcoming). Chicago Press.

• Yang, X. F. et al. (2024). "Transcendent thinking counteracts longitudinal effects of mid‐adolescent exposure to community violence in the anterior cingulate cortex". Journal of Research on Adolescence. 

• Falk, A., Kosse, F., Pinger, P., Schildberg-Hörisch, H., and Deckers, T. (2021). "Socioeconomic Status and Inequalities in Children’s IQ and Economic Preferences". Journal of Political Economy, 129(9), 2504-2545. 

• Hwang, J., and Phillips, D. J. (2020). "Entrepreneurship as a response to Labor market Discrimination for formerly incarcerated people". In Academy of Management Proceedings (Vol. 2020, No. 1, p. 18636).

So, is the future of University Teaching an AI teaching platform where it allows academics to upload their lecture presentations, which are then turned into text notes by the AI? Moreover, lets say this technology also creates an avatar based on the photo of the lecturer which allows the avatar to deliver a virtual class from the notes on behalf of the academic. As a result, a director of such a technology can argue that: "We take the experience and expertise of the lecturers, which is all summarised in their PowerPoint presentations, and then we have a property designed lesson". 

What are the main problems with respect to the learning and teaching process in the above example?  

The experience and expertise of lecturers are important elements in conveying learning objectives and explaining the underline economic theory. In particular, for the effective dissemination of knowledge in class, the characteristic of lecturers are the main drivers of the learning process; in other words a kind, charismatic, accessible, encouraging and inspiring lecturer cannot be replaced by any AI-type avatar. Furthermore, the necessity of incorporating technological innovation as a tool for enhancing pedagogy should not be undermining the importance of charismatic teaching. Therefore, the use of technological tools offered by AI applications can be employed with the purpose of facilitating and promoting a positive learning environment which is well interlinked with current trends in the labor market.  

Recent technological developments in AI and big data provide plethora of opportunities to incorporate LLMs in education activities for the purpose of improving and enhancing the teaching and learning experience both for educators as well as for students. Nevertheless, despite the capabilities that these models offer especially in organizing learning processes, we should be aware of their pitfalls and limitations with respect to the role of educators in fostering human interactions, dialogue and transmission of knowledge (see, Gan et al, 2023). Moreover, examination methods (e.g., summative and formative assessments) should be designed in such a way to ensure that educators can evaluate the learning abilities of students based on their true skills, without interference from LLMs, using: (i) in-person written final examinations, (ii) presentations for group or individual projects (iii) assigning a small percentage of a module's final grade for class participation, (iv) designing assessments and tasks such that these can capture the understanding of learners on the taught material.   

Related Literature:

• Borg, M. O., and Stranahan, H. A. (2010). "Evidence on the Relationship between Economics and Critical Thinking Skills". Contemporary Economic Policy, 28(1), 80-93. 

• Gan, W., Qi, Z., Wu, J., and Lin, J. C. W. (2023). "Large Language Models in Education: Vision and Opportunities". In 2023 IEEE International Conference on Big Data (BigData) (pp. 4776-4785). IEEE. 

Books:

• McMillan, J. (2014). The End of Banking: Money, Credit, and the Digital Revolution. ‎Zero/One Economics.  

• Sandel, M. J. (2013). What Money Can’t Buy: The Moral Limits of Markets. ‎ Farrar, Straus and Giroux; (Reprint edition).