The Role of Digital, Formative Testing in e-Learning for Mathematics: A Case Study in the Netherlands

Tempelaar, Dirk; Kuperus, Boudewijn T. A.; Cuypers, Hans; Kooij, Henk van der; Vrie, Evert van de; Heck, André

doi:10.7238/rusc.v9i1.1272

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…High school systems in Europe differ strongly, most particularly in the teaching of mathematics and statistics. Therefore, it is crucial that the first module offered to these students is flexible and allows for individual learning paths (Tempelaar, et al, , 2013Tempelaar, Kuperus, et al, 2012). In the investigated course, students work an average 32.6 hours in MML and 20.7 hours in MSL, 30% to 40% of the available time of 80 hours for learning in both topics.…”

Section: Participantsmentioning

confidence: 99%

“…Exactly two third of the students has a basic mathematics level, one third has an advanced level. (See Tempelaar, et al, 2009Tempelaar, Kuperus, et al, 2012 for detailed description. )…”

Section: Registration Systems Capturing Demographic Datamentioning

confidence: 99%

“…According to Bienkowski et al (2012, p. 5), "education is getting very close to a time when personalisation will become commonplace in learning", although several researchers (Greller and Drachsler, 2012;Stiles, 2012) indicate that most institutions may not be ready to exploit the variety of available datasets for learning and teaching. Many learning analytics applications use data generated from learner activities, such as the number of clicks (Siemens, 2013;Wolff, Zdrahal, Nikolov, and Pantucek, 2013), learner participation in discussion forums (AgudoPeregrina, Iglesias-Pradas, Conde-González, and Hernández-García, 2014;Macfadyen and Dawson, 2010), or (continuous) computer-assisted formative assessments (Tempelaar, Heck, Cuypers, van der Kooij, and van de Vrie, 2013;Tempelaar, Kuperus et al, 2012;Wolff et al, 2013). User behaviour data are frequently supplemented with background data retrieved from learning management systems (LMS) (Macfadyen and Dawson, 2010) and other student admission systems, such as accounts of prior education (Arbaugh, 2014;Richardson, 2012;Tempelaar, Niculescu, Rienties, Giesbers, and Gijselaers, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stability and Sensitivity of Learning Analytics based Prediction Models

Tempelaar

Rienties²,

Giesbers

2015

Proceedings of the 7th International Conference on Computer Supported Education

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Abstract:Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. In this follow-up study of previous research (Tempelaar, Rienties, and Giesbers, 2015), we focus on the issues of stability and sensitivity of Learning Analytics (LA) based prediction models. Do predictions models stay intact, when the instructional context is repeated in a new cohort of students, and do predictions models indeed change, when relevant aspects of the instructional context are adapted? This empirical contribution provides an application of Buckingham Shum and Deakin Crick's theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computerassisted, formative assessments and LMSs. We compare two cohorts of a large introductory quantitative methods module, with 1005 students in the '13/'14 cohort, and 1006 students in the '14/'15 cohort. Both modules were based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials, and have similar instructional design, except for an intervention into the design of quizzes administered in the module. Focusing on the predictive power, we provide evidence of both stability and sensitivity of regression type prediction models.

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Section: Participantsmentioning

confidence: 99%

“…Exactly two third of the students has a basic mathematics level, one third has an advanced level. (See Tempelaar, et al, 2009Tempelaar, Kuperus, et al, 2012 for detailed description. )…”

Section: Registration Systems Capturing Demographic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stability and Sensitivity of Learning Analytics based Prediction Models

Tempelaar

Rienties²,

Giesbers

2015

Proceedings of the 7th International Conference on Computer Supported Education

Self Cite

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“…Assessment in mathematics needs to be well structured and sequenced. Tempelaar, Kuperus, Cuypers, Kooij, Vrie, and Heck (2012) then is prompted to go to the next question. However, it is not enough for feedback to be auto-generated by a computer program.…”

Section: Enable Effective Assessment and Feedbackmentioning

confidence: 99%

An e-Learning Model for Teaching Mathematics on an Open Source Learning Platform

Ahn

Edwin

2018

IRRODL

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Throughout the world, mathematics plays a vital role for the educational and developmental aspirations of any country. The quest to teach mathematical knowledge in a viable and effective way so as to induce creativity and applicability among learners is an ongoing challenge, especially for developing countries. A better understanding of how students learn mathematics coupled with effective application of mathematical e-learning can enhance meaningful learning of mathematics and make the subject more exciting. In this note, we introduce a mathematical e-learning model suitable for the modern digital era based on the learning theories of social constructivism, social realism, and connectivity. We then discuss the feasibility of implementing the model on an open source e-learning platform. Our findings reveal that the platform offers a developer’s tool for coding and customizing templates to attain higher levels of usage and interactivity in which learners can create and control learning objects while they observe the results.

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“…Structuring the learning process by using formative assessment types, especially in mathematics courses where student success is low, can also increase the quality of teaching. There are studies in the literature indicating that formative assessment has positive effects on success, attitude, and remembering what has been learned in mathematics lessons (Tekin 2010b, Tempelaar et al, 2012. In addition, formative assessment contributes to the development of students' metacognitive awareness as it gives them the opportunity to evaluate themselves and monitor their individual development (Jones, 2007).…”

Section: Introductionmentioning

confidence: 99%

Opinions of Mathematics Teachers on Measurement and Evaluation

PEKER,

ACAR

2024

akef

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The purpose of this study is to examine the opinions of mathematics teachers comprehensively about measurement and evaluation. The study was conducted as a case study. The study group consists of 15 volunteer mathematics teachers working in schools in various regions of Turkey in the 2021–2022 academic year. A semi-structured interview method was used to collect research data, and a content analysis technique was used to analyze the data. The results of the study showed that mathematics teachers do not have enough information about the purposes of measurement and evaluation, and they generally think product-oriented. It was determined that teachers mostly use multiple-choice and open-ended questions in written exams and they care most about in-class participation and homework while giving performance grades. Also, it was concluded that the teachers paid attention to one or a few issues in the evaluation of the project assignments and could not complete the evaluation. It was determined that mathematics teachers used traditional measurement and evaluation techniques and did not have adequate knowledge about alternative measurement and evaluation techniques. Based on the results obtained of the study, various suggestions were made to implementers and researchers.

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The Role of Digital, Formative Testing in e-Learning for Mathematics: A Case Study in the Netherlands

Cited by 7 publications

References 8 publications

Stability and Sensitivity of Learning Analytics based Prediction Models

Stability and Sensitivity of Learning Analytics based Prediction Models

An e-Learning Model for Teaching Mathematics on an Open Source Learning Platform

Opinions of Mathematics Teachers on Measurement and Evaluation

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