On the impact of adaptive test question selection for learning efficiency

Barla, Michal; Bieliková, Mária; Ezzeddinne, Anna Bou; Kramár, Tomáš; Šimko, Marián; Vozár, Oto

doi:10.1016/j.compedu.2010.03.016

Cited by 69 publications

(44 citation statements)

References 8 publications

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“…However, only few systems use intelligent methods for exercise item allocation (Barla et al, 2010). The use of intelligent item allocation algorithms (IAA) is, however, a common practice in testing.…”

Section: Web-based Learning Environmentsmentioning

confidence: 99%

Difference in Learning Among Students Doing Pen-and-Paper Homework Compared to Web-Based Homework in an Introductory Statistics Course

Stefansson

Björnsdóttir

2017

Journal of Statistics Education

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A repeated crossover experiment comparing learning among students handing in pen-and-paper homework (PPH) with students handing in web-based homework (WBH) has been conducted. The system used in the experiments, the tutor-web, has been used to deliver homework problems to thousands of students in mathematics and statistics over several years. Since 2011 experimental changes have been made regarding how the system allocates items to students, how grading is done and the type of feedback provided. The experiment described here was conducted annually from 2011 to 2014. Approximately 100 students in an introductory statistics course participated each year. The main goals were to determine whether the above mentioned changes had an impact on learning as measured by test scores in addition to comparing learning among students doing PPH with students handing in WBH.The difference in learning between students doing WBH compared to PPH, measured by test scores, increased significantly from 2011 to 2014 with an effect size of 0.634. This is a strong indication that the changes made in the NAMEOFSYSTEM have a positive impact on learning.Using the data from 2014 a significant difference in learning between WBH and PPH for 2014 was detected with an effect size of 0.416 supporting the use of WBH as a learning tool.

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Section: Web-based Learning Environmentsmentioning

confidence: 99%

Difference in Learning Among Students Doing Pen-and-Paper Homework Compared to Web-Based Homework in an Introductory Statistics Course

Stefansson

Björnsdóttir

2017

Journal of Statistics Education

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“…Hence, the selection of challenging tasks is not only supposed to enhance motivation, but could also have an effect on learning. In addition to that, prior research found that learner's characteristics and, in particular, the learner's proficiency can influence learning outcomes [38,[47][48][49][50] and the need for any enhancement to the basic learning material, such as adaptive task sequencing [51][52][53][54][55]. The overall finding is that students with low proficiency benefit more from adaptive learning environments than do students with high proficiency [38,53,55].…”

Section: Adaptive Item Sequencing In Item-based Learning Environmentsmentioning

confidence: 71%

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

Moeyaert

Wauters²,

Desmet

et al. 2016

Systems

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Abstract:The research on electronic learning environments has evolved towards creating adaptive learning environments. In this study, the focus is on adaptive curriculum sequencing, in particular, the efficacy of an adaptive curriculum sequencing algorithm based on matching the item difficulty level to the learner's proficiency level. We therefore explored the effect of the relative difficulty level on learning outcome and motivation. Results indicate that, for learning environments consisting of questions focusing on just one dimension and with knowledge of correct response, it does not matter whether we present easy, moderate or difficult items or whether we present the items with a random mix of difficulty levels, regarding both learning and motivation.

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“…This approach ensures that target probability will change with the time and adjust the difficulty accordingly. Item Response Theory [21], method for adaptive evaluation of item from psychometrics, have been effectively applied to different Computer Adaptive Testing [11,22]. Item Response theory takes into account of individual's knowledge, difficulty level of a particular test item, and the probability of correct response for that particular item by the individual.…”

Section: Related Workmentioning

confidence: 99%

“…Item Response Theory, based on course structure and appropriate topics to select the k-best questions with adequate difficulty for a particular learner to attain adaptiveness, was brought into focus by Barla [11]. Stackelberg game theoretic model was also applied to select effective and randomized test questions [12] for large scale, public exams i.e., (driver's license test, Toefl iBT).…”

Section: Introductionmentioning

confidence: 99%

Optimal Weighting for Exam Composition

Ganzfried

Yusuf

2018

Education Sciences

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A problem faced by many instructors is that of designing exams that accurately assess the abilities of the students. Typically, these exams are prepared several days in advance, and generic question scores are used based on rough approximation of the question difficulty and length. For example, for a recent class taught by the author, there were 30 multiple choice questions worth 3 points, 15 true/false with explanation questions worth 4 points, and 5 analytical exercises worth 10 points. We describe a novel framework where algorithms from machine learning are used to modify the exam question weights in order to optimize the exam scores, using the overall final score as a proxy for a student's true ability. We show that significant error reduction can be obtained by our approach over standard weighting schemes, i.e., for the final and midterm exam, the mean absolute error for prediction decreases by 90.58% and 97.70% for linear regression approach respectively resulting in better estimation. We make several new observations regarding the properties of the "good" and "bad" exam questions that can have impact on the design of improved future evaluation methods.

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On the impact of adaptive test question selection for learning efficiency

Cited by 69 publications

References 8 publications

Difference in Learning Among Students Doing Pen-and-Paper Homework Compared to Web-Based Homework in an Introductory Statistics Course

Difference in Learning Among Students Doing Pen-and-Paper Homework Compared to Web-Based Homework in an Introductory Statistics Course

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

Optimal Weighting for Exam Composition

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