cpm.4.CSE/IRT: Compact process model for measuring competences in computer science education based on IRT models

Zendler, Andreas

doi:10.1007/s10639-018-9794-3

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…It is important to see from Figures 5 that b3.1 starts without first estimating model parameters, i.e. the statistical tests are not influenced by estimated parameters, as is the case in B3.1 through B3.5 of cpm.4.CSE/IRT (see Zendler, 2018).…”

Section: Sub Process B31 Test Model Assumptions Statistically By Non-parametric Tests -mentioning

confidence: 98%

“…The principle of such tests is to compare the observed data matrix (data set with the responses of persons to items) with a sample of simulated data matrices in order to calculate statistics to test the most important assumptions of the Rasch model (monotonically increasing item characteristics, local stochastic independence, one-dimensionality, specific objectivity) (see Verhelst, 2008;Koller & Hatzinger, 2013;Verhelst, Hatzinger, & Mair, 2007). cpm.4.CSE/IRT (compact process model for Competence Science Education based on IRT models) is a process model for competence measurement based on IRT models (Zendler, 2018). It allows the efficient development of measuring instruments for computer science education.…”

Section: Cpm4cse/irtmentioning

confidence: 99%

“…Figure 1). In more detail IDEFO is described with cpm.4.CSE/IRT (see Zendler, 2018). 2019) is used for the implementation of cpm.4.CRT/IRT N=small .…”

Section: International Journal Of Research Studies In Psychology 83mentioning

confidence: 99%

“…In analogy to cpm.4.CSE/IRT (Zendler, 2018), sub process b3.4 provide model information graphically is executed. The output of sub process b3.4 is depicted in Figure 6.…”

Section: A) Item Characteristics Curves B) Item Information Functions C) Test Information Functionmentioning

confidence: 99%

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cpm.4.CSE/IRT N=small: A companion to cpm.4.CSE/IRT for N = small

Zendler¹

2019

IJRSP

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cpm.4.CSE/IRT N=small (compact process model for Competence Science Education based on IRT for small samples) is a process model for competence measurement based on IRT models that is optimized for small sample sizes. cpm.4.CSE/IRT/ N=small is a supplementary to cpm.4.CSE/IRT and consists of the four sub processes B1 determine items, B2 test items, B3 N=small analyze items according to Rasch model, and B4 interpret items by criteria. It is also modeled in IDEF0 and implemented in R. The difference between cpm.4.CSE/IRT and cpm.4.CSE/IRT N=small is that the processes of B3 N=small are rearranged, and sub process b3.1 test model assumptions statistically by non-parametric tests replaces B3.3 test model assumptions statistically. With cpm.4.CSE/IRT N=small it is possible to develop measuring instruments for computer science education even with small samples (N = small).

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Section: Sub Process B31 Test Model Assumptions Statistically By Non-parametric Tests -mentioning

confidence: 98%

Section: Cpm4cse/irtmentioning

confidence: 99%

“…Figure 1). In more detail IDEFO is described with cpm.4.CSE/IRT (see Zendler, 2018). 2019) is used for the implementation of cpm.4.CRT/IRT N=small .…”

Section: International Journal Of Research Studies In Psychology 83mentioning

confidence: 99%

“…In analogy to cpm.4.CSE/IRT (Zendler, 2018), sub process b3.4 provide model information graphically is executed. The output of sub process b3.4 is depicted in Figure 6.…”

Section: A) Item Characteristics Curves B) Item Information Functions C) Test Information Functionmentioning

confidence: 99%

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cpm.4.CSE/IRT N=small: A companion to cpm.4.CSE/IRT for N = small

Zendler¹

2019

IJRSP

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“…Lastly, it reports a variety of fit statistics that allows for more thorough item evaluation (Bond & Fox, 2001;Boone, Staver, & Yale, 2013). A number of researchers have advocated specifically for IRT approaches to be utilized in the development of CS assessments (Werner, Denner, Campe, & Kawamoto, 2012;Winters & Payne, 2005;Zendler, 2019). In addition, utilization of both evidence-centered design for item development and Rasch modeling (a form of IRT) for validation has effectively been used for a focused CS concept inventory (only for control structures understanding) development for grades 7-10 (Mühling, Ruf, & Hubwieser, 2015), and provides an example for this research to utilize.…”

Section: Current Workmentioning

confidence: 99%

Development and Validation of the Middle Grades Computer Science Concept Inventory (MG-CSCI) Assessment

Rachmatullah

Akram

Boulden

et al. 2020

EURASIA J MATH SCI T

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The increasing interest in computer science (CS) and CS-integrated STEM teaching and learning has created a need for assessment instruments that can be used to evaluate the efficacy of innovative instructional approaches to K-12 CS education. However, there is a lack of validated assessment tools aligned to core CS concepts for younger students. This paper reports on the development and validation of a CS concept assessment for middle grades (ages 11-13) students. A total of 27 multiple-choice items were developed, guided by focal knowledge, skills and abilities associated with the concepts of variables, loops, conditionals, and algorithms. These items were administered to 457 middle grades students. The items were presented in form of block-based programming code and administered in a week-long computational modeling intervention. A combination of classical test theory and item response theory approaches were used to validate the assessment. Based on results, it was found that only 24 items are considered valid and reliable items to measure CS conceptual understanding. The results also suggested that the assessment can be used as a pre and post-test to investigate students' learning gains. This work fills an important gap by providing a key resource for researchers and practitioners interested in assessing middle grades student CS conceptual understanding.

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