Adaptive test system based on revised Bloom's taxonomy

Raykova, Mariana; Kostadinova, Hristina; Totkov, George

doi:10.1145/2023607.2023692

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…One possible approach to starting question banks without any test items in the concrete subject area is to use the so-called template AQT independent from the concrete subject area, such as [9]:…”

Section: Accumulative Question Type In Moodlementioning

confidence: 99%

Automatized test item generation in moodle

Raykova

Kostadinova

Totkov

2014

Proceedings of the 15th International Conference on Computer Systems and Technologies

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Creating reliable, reusable and efficient tests is a long and time-consuming process. One of the most important stages is creating a question bank. An approach to automatized generation of test items of different types is presented in this paper. It is based on the accumulative method that uses stored and assessed test items in the process of doing the test. An important role is played by the so-called accumulative question type (AQT), which, along with its attributes is described as a base of design, specifications and realization of a software test module implemented in Moodle. The presented module provides possibilities to enrich question banks with test items of different types and use the learning and assessing process in automatized test generation. INTRODUCTIONCreating standardized, efficient and reusable e-tests is a difficult and time-consuming process [1]. The basic challenge is to develop a question bank that consists of a great variety of test items of different types. Automatized test item generation is an appropriate approach to overcome this difficulty. There are traditional methods based on computer linguistic algorithms [7], Item Response Theory [8], semantic web technologies [2, 4], etc.Another technique is based on previous students' experience -accumulative assessment [11,12]. This method can be used for assessment of higher levels of knowledge, as a mechanism for dynamic calibration of question banks, generation of items from other types and adaptive testing [11,5,6,3]. This approach utilizes the so-called accumulative question type (AQT) -test item of the 'essay' type in the process of testing in order to use stored and assessed answers to a question to automate the generation of new test items of different types.The most important stages during the process of implementation of this method are:To design the AQT model -determine and describe AQT attributes;To determine the place of the AQT within the variety of the set of traditional types of test items used during the learning process; To develop the AQT software module (including creating, editing, assessing, etc.) of AQT in an e-learning environment (eLE); To develop and implement means for generating test items of different types based on stored and assessed answers of the AQT and transforming test items of different types used in the standard question bank of the corresponding eLE; The categorization of the AQT according to traditional Ruter, Herbig and IMS QTI classifications of test items shows that the AQT can be used to generate all types of conventional question types [10]. This categorization renders an account of all the attributes of the AQT, such as: the number of the open positions (slots: 1, 2, more or equal to 3); format of the slots: text, file or image and possible question types that can be generated by the concrete AQT. Creating such a test item framework is an important step in the process of design, development and implementation of software module that uses the idea of automatized test creation.

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Section: Accumulative Question Type In Moodlementioning

confidence: 99%

Automatized test item generation in moodle

Raykova

Kostadinova

Totkov

2014

Proceedings of the 15th International Conference on Computer Systems and Technologies

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“…This element was designed as a progressive assessment process, where students who answered a question correctly would be directed to a more challenging conceptual question and those who did not select the correct answer would be steered to an easier question. Hence, we needed to rate the difficulty of the question -which became a challenging task in itself [7].…”

Section: Introductionmentioning

confidence: 99%

Comparing Expert Predictions to Student Performance on Challenging Conceptual Questions: Towards an Adaptive Learning Module for Materials Science

Nigon,

Simionescu,

Ekstedt

et al.

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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Thomas Ekstedt is a software developer in the School of Chemical, Biological and Environmental Engineering at Oregon State University. He is involved in the development of technology-based educational systems, particularly in the areas of concept-based instruction, adaptive learning, and interactive simulation of physical phenomena. Julie TuckerDr. Tucker earned her B.S. in Nuclear Engineering from the University of Missouri -Rolla. She attended graduate school at the University of Wisconsin -Madison as a Naval Nuclear Propulsion Fellow, where she received her M.S. and Ph.D. in Nuclear Engineering with a minor in Materials Science in 2008. After graduation, Dr. Tucker spent five years as a Principal Scientist at Knolls Atomic Power Laboratory in Schenectady, NY studying the thermal stability of structural alloys for nuclear power systems. She joined the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University as an Assistant Professor in 2013 and was promoted to Associate Professor in 2019. In 2019 she was awarded the Dean's Professorship and was also appointed as the Materials Science Interdisciplinary Graduate Program Director. Dr. Tucker has an active research group focused on degradation of materials in extreme environments and alloy development. Her research efforts leverage both modeling and experimental approaches to gain fundamental understanding of materials performance. Milo KoretskyMilo Koretsky (he/him/his) is the McDonnell Family Bridge Professor holding a joint appointment in Chemical and Biological Engineering and Education at Tufts University. He received his BS and MS degrees from UC San Diego and his PhD from UC Berkeley, all in chemical engineering. He is interested in integrating technology into effective educational practices and in promoting the use of higher-level cognitive and social skills in engineering problem solving.

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