Using real data for statistics education in an open‐source learning environment

Jonsdottir, Anna Helga; Lentin, Jamie; Calian, Violeta; Hafsteinsson, Eggert K.

doi:10.1111/test.12237

Cited by 3 publications

(2 citation statements)

References 6 publications

(6 reference statements)

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“…It would be expected that the combined approach should be better than (c) alone, but requires considerably greater time (and dedicated programming) to set up. This type of approach has been used in [8] where real data sets are mined to extract random subsets to be used in random questions.…”

Section: Data Requirements For Further Researchmentioning

confidence: 99%

Identifying Rote Learning and the Supporting Effects of Hints in Drills

Stefansson¹,

Jonsdottir²,

Jonmundsson³

et al. 2021

INTED2021 Proceedings

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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 questions 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 tutorweb 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.

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Section: Data Requirements For Further Researchmentioning

confidence: 99%

Identifying Rote Learning and the Supporting Effects of Hints in Drills

Stefansson¹,

Jonsdottir²,

Jonmundsson³

et al. 2021

INTED2021 Proceedings

Self Cite

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“…Some features of the system that are worth mentioning but not discussed further here are the possibility to use real data in exercises (uploaded by the user or by linking to open data sources) and that students earn SmileyCoin, a cryptocurrency, while studying. Descriptions of these features can be found in [2] and [3].…”

Section: The Tutor-webmentioning

confidence: 99%

The Effect of the Number of Distractors and the "None of the Above" - "All of the Above" Options in Multiple Choice Questions

Jonsdottir¹,

Jonmundsson²,

Armann³

et al. 2021

INTED2021 Proceedings

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Multiple choice questions (MCQs) are commonly used for assessment in higher education. With increased use of on-line examination it is likely that the usage of MCQs will be even more in years to come. It is therefore of interest to examine some characteristics of these type of questions such as the effect of the number of distractors used and the "None of the above" (NOTA) or "All of the above" (AOTA) options.The tutor-web is an open-source, on-line drilling system that is freely available to anyone having access to the Internet. The system was designed to be used for teaching mathematics and statistics but can in principle be used for other subjects as well. The system offers thousands of multiple choice questions at high school and university level. In addition to be a tool used by students for learning it has also been used as a testbed for research on web-assisted education.The tutor-web system was used both as a learning tool and as a testing tool in a university course on mathematical statistics in the spring of 2020. Around 300 students were enrolled in the course providing tens of thousands of answers to MCQs designed to investigate the effect of the number of distractors and the use of NOTA and AOTA options in questions. The main findings of the study were that the probability of answering a question correctly was highest when a AOTA option was used as a distractor and when NOTA and AOTA were not used in questions. The probability of answering a question correctly decreased with the number of distractors.

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