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IDENTIFYING ROTE LEARNING AND THE SUPPORTING EFFECTS OF HINTS IN DRILLS
University of Iceland, Science Institute (ICELAND)
About this paper:
Appears in: INTED2021 Proceedings
Publication year: 2021
Pages: 2605-2611
ISBN: 978-84-09-27666-0
ISSN: 2340-1079
doi: 10.21125/inted.2021.0556
Conference name: 15th International Technology, Education and Development Conference
Dates: 8-9 March, 2021
Location: Online Conference
Abstract:
Whenever students use any drilling system the question arises how much of their learning is meaningful learning, which emphasises understanding and the transferability of prior knowledge, and how much is memorisation through repetition or rote learning. Although both types of learning have their place in an educational system it is important to be able to distinguish between these two approaches to learning and identify options which can dislodge students from rote learning and motivate them towards meaningful learning.

The tutor-web is an online drilling system which has been used by thousands of students from Iceland to Kenya. The design aim of the system is to promote meaningful learning rather than evaluation. This is done by presenting students with multiple-choice question which are selected randomly but nevertheless linked to the students’ performance to ensure that students are appropriately challenged. The questions themselves can be generated for a specific topic by drawing correct and incorrect answers from a collection associated with a general problem statement or heading. With this generating process students may see the same question heading twice but be presented with all new answer options or a mixture of new and old answer options.

Data from an introductory university course on probability theory and statistics, taught using the tutor-web during COVID-19, are analysed to separate rote learning from meaningful learning. The analyses show that considerable non-rote learning takes place, but even with fairly large question databases, students’ performance is considerably better when they are presented with an answer option they have seen before. An element of rote learning is thus clearly exhibited but a deeper learning is also demonstrated.

The item database has been seeded with occasional hints such that some questions contain fairly detailed clues, which should cue the students towards the correct answer. This ties in with the issue of meaningful learning versus rote learning since the hope is that a new hint will work as a cue to coax the student to think harder about the question rather than continue to employ rote learning. The existence of occasional hints allows several comparisons. The simplest analysis is on whether the overall grade on cue questions is higher than on the non-cue questions. A more important issue is whether more learning has occurred and methods are developed to estimate the change rather than status. Preliminary results indicate that hints are particularly useful for students with poor performance metrics, and a power analysis demonstrates the sample sizes needed in future studies to better quantify these effects.
Keywords:
Identifying rote learning, applying learning to new problems, drills with clues, personalised drills.