The purpose of teaching is to facilitate learning and make the learning process more effective. While adapting teaching to students’ learning styles may not bring the desired results, as the adopted strategies are not always the most efficient, teaching in accordance with students’ preferences may at least improve students’ well-being. 

Existing studies have already extensively examined which teaching and learning strategies are effective. Ideally, the methods that have been scientifically proven to be effective would be those students prefer. Knowing the students’ preferences for specific learning and teaching strategies can help improve understanding of their behaviour and keep learners actively involved in the learning process. This in turn may lead to improved competency and better academic achievement.

The study examines students’ preferences towards learning strategies with varying degrees of effectiveness and explores preference heterogeneity patterns across subgroups defined by students’ observed characteristics (gender, academic result) as well as time left to final exams. We hypothesise that although evidence-based learning and teching strategies are often not practiced at schools, students have preferences towards them. Students' preferences towards learnn and teaching strategies may differ due to their proficiency as they may differ from the poor ones in type and regularity of practicing learning strategies (Shaffie et al. 2020), but also due to their age ( Magogwe & Oliver, 2007), and gender. Goh & Foong (1997) found that compensation and affective strategies were significantly more common among female than male students.Moreover, we aim to reveal how preerences may differ due to the conditions of education (pandemic and post-pandemic) and the moment in education (lower or final grade).

Preferences refer to a certain characteristics people like or want to have. Following common in economic theory assumption (Hausman, 2011; Nitzan, 2009), that there exists relationship between peoples’ preferences and their behavior, we can say that learning preferences relate to the tendency of students to choose the way they learn. Knowing preferences enables us to explain and predict behaviours including those related to the learning process. The preferences may differ between people due to their observable characteristics such as gender, age, type and level of education, learning goals, learning situation as well as not observable motives.

Learning strategies relate to a set of approaches and actions taken in the learning process to effectively obtain, process and retain information and skill for later use (Lublin, 2003). There are several classifications of learning strategies (Shi, 2017); they refer to both actions taken by the teacher, such as instructional delivery and the students, and may concern the organization of learning, methods of repetition, and assimilation of material and assessment. We consider the available evidence for the learning strategies widely advocated in recent years, starting with those implemented by students without assistance, including strategies analyzed by Dunlosky et al. (2013).

Literature in cognitive psychology has established learning strategies that are most effective at promoting long-term learning. The strategies with strongest empirical support are self-testing and distributing study activities over time (Black & Allen, 2019). Still, many students rely on ineffective strategies such as rereading or highlighting important points in the text (Karpicke & Blunt, 2011), which may result from the illusion of competence. Effective strategies may also be underutilized because teachers do not know them and hence also students do not implement them (Dunlosky et al. 2013) or due to numerous myths that exist about them.

In our study we consider strategies learning strategies such as mind maps, retreval practice, and traditional methods of studying. Based on the existing studies, we are able to order them according to their effectiveness Moreover we focus on the mode of class delivery (remoty, hybrid, stationary) and forms off assessment.

The data was collected as part of the TICKS study conducted in Warsaw, the capital city of Poland, on a representative sample of secondary school students (including high schools, technical and vocational schools). In the paper, we use data from the last two editions of the TICKS, conducted in 2021 and 2022, respectively. The study was conducted via the Computer-Assisted Web Interviewing (CAWI) technique. The questionnaire administered to students consisted of three parts. The first one focused on the general background of the students. This was followed by math, science and reading comprehension assessments, which methodologically referred to the PISA study, and finally, a Discrete Choice Experiment (DCE). Thanks to the simultaneous analysis of competences and preferences, we were able to relate students’ preferences in terms of learning strategies with their actual educational results. We use DCE to investigate the preferences of students for learning and teaching strategies. DCE is a stated preference method in which respondents make choices in hypothetical situations. The DCE approach is embedded in random utility theory (McFadden, 1974; Train, 2009) In DCE each respondent was presented with a series of hypothetical choice situations consisting of two alternatives. In each situation, the students were asked to choose the preferred way of organization of the course they would attend in the next semester described by few attributes. The attributes identified as relevant to describe the learning and teaching strategies included the mode of class organization, the dominant way of working during classes, the type of assessment, how students learn, and time students spent on learning. The alternative with the desired feature should increase the utility associated with this alternative. In the selection process, respondents make a trade-off between two or more features that are assumed to generate positive utility. To estimate the utility and the trade-off respondents would make to study in a specific way we apply Multinomial Logistic Regression, Random Parameter Logit Model and Latent CLass analysis. The later two are used to address the issue of heterogeneity of preerences.

Having data for two periods we find that in 2022 students shifted their priorities towards in-person education. Hybrid learning remained the preferred option over in-person schooling; however, the preferences towards this form became weaker. For the remote classes, we observed a reversal of preferences - students reported an aversion to this mode of study. There is also a certain adjustment of preferences for assessment; the strength of preferences and reluctance to use methods other than tests with open questions decreased in the post-pandemic period. Research has shown that, indeed, students prefer learning strategies that show the highest effectiveness. The level of academic achievement was not found to be associated with preferences toward more effective strategies. Testing, a form of retrieval practice that promotes better long-term retention than rereading, note-taking, or creating mind or concept maps, is also the most preferred way for students to learn. Although, as it turns out, testing is not only one of the most effective strategies for consolidating knowledge but also preferred by students, teachers use it mostly as an assessment rather than a learning tool. The differences in preferences arise for the type of assessment. Although males and females exhibit the same pattern, value multiple-choice tests and group projects more than open-question tests, and present negative attitudes towards oral responses, females have stronger preferences towards the first two and greater aversion to the latter than males. As one might expect, top-performing students are significantly more willing to have classes where the teacher mainly presents the material rather than group working.

Abi-El-Mona I, Adb-El-Khalick F (2008) The influence of mind mapping on eighth graders’ science achievement. School Sci Math 108: 298–312 10.1111/j.1949-8594.2008.tb17843 Bawaneh, A. K. (2019). The effectiveness of using mind mapping on tenth grade students’ immediate achievement and retention of electric energy concepts. Journal of Turkish Science Education, 16(1), 123-138. Black, S., & Allen, J. D. (2019). Part 11: Learning Strategies. The Reference Librarian, 60(4), 288-303. Buran, A., & Filyukov, A. (2015). Mind mapping technique in language learning. Procedia-Social and Behavioral Sciences, 206, 215-218. Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the public interest, 14(1), 4-58. Goh, C., & Foong, K. P. (1997). Chinese ESL students’ learning strategies: A look at frequency, proficiency, and gender. Hong Kong Journal of Applied Linguistics, 2(1), 39-53. Hausman, D. M. (2011). Preference, value, choice, and welfare. Cambridge University Press. Karpicke, J. D., & Blunt, J. R. (2011). Response to comment on “retrieval practice produces more learning than elaborative studying with concept mapping”. Science, 334(6055), 453-453. Karpicke, J. D., Butler, A. C., & Roediger III, H. L. (2009). Metacognitive strategies in student learning: do students practise retrieval when they study on their own?. Memory, 17(4), 471-479. Long, D. J., & Carlson, D. (2011). Mind the map: How thinking maps affect student achievement. Networks: An Online Journal for Teacher Research, 13(2), 262-262. Lublin, J. (2003). Deep, surface and strategic approaches to learning. Centre for teaching and learning, 806-825. Machado, C. T., & Carvalho, A. A. (2020). Concept mapping: Benefits and challenges in higher education. The Journal of Continuing Higher Education, 68(1), 38-53. Nashir, M., & Laili, R. N. (2021). Hybrid Learning as an Effective Learning Solution on Intensive English Program in the New Normal Era. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 9(2), 220232. Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of educational research, 76(3), 413-448. Nitzan, S. (2009). Collective preference and choice. Cambridge University Press. Shaffie, N., ZIN, R. M., & ISMAIL, S. (2020). ACCOUNTING STUDENTS’PREFERENCES TOWARDS LEARNING STRATEGIES IN UNIVERSITI MALAYSIA TERENGGANU. Universiti Malaysia Terengganu Journal of Undergraduate Research, 2(4), 75-88.