A Classification of Adaptive Feedback in Educational Systems for Programming

Le, Nguyen-Thinh

doi:10.3390/systems4020022

Cited by 31 publications

(16 citation statements)

References 38 publications

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“…Although this automated feedback works well with questions that have standardized answers, it still lacks the just-in-time feedback and guidance that instructors in the classroom can provide. Adaptive feedback could be a good solution for providing feedback in an asynchronous online learning environment, as it not only verifies the correctness of an answer but also provides different information for different answers (Bimba et al, 2017;Dempsey & Sales, 1993;Le, 2016).…”

Section: Methodsmentioning

confidence: 99%

Designing and Developing Videos for Online Learning: A Seven-Principle Model

Joyner

Goel

2019

OLJ

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Despite the ubiquitous use of instructional videos in both formal and informal learning settings, questions remain largely unanswered on how to design and develop video lessons that are often used as the primary method for delivering instruction in online courses. In this study, we experimented with a model of seven principles drawn from instructional design theories for designing and developing video lessons for an online graduate course. Feedback was collected from students through surveys on their perceptions of the effectiveness of the video lessons and the overall course quality for eight semesters. This paper shares the instructors’ experience on the design and development of the video lessons as well as the survey findings. Implications of the findings for instructional design and future research are also discussed.

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Section: Methodsmentioning

confidence: 99%

Designing and Developing Videos for Online Learning: A Seven-Principle Model

Joyner

Goel

2019

OLJ

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“…Previous researchers have conducted reviews of adaptive feedback systems. Le (2016) analyzed the approaches used in developing educational systems for programming and introduced a classification for adaptive feedback supported by these systems. Hepplestone, Holden, Irwin, Parkin, and Thorpe (2011) explored various literature supporting the appropriate use of technology for providing feedback to students.…”

Section: Introductionmentioning

confidence: 99%

Adaptive feedback in computer-based learning environments: a review

et al. 2017

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Adaptive support within a learning environment is useful because most learners have different personal characteristics such as prior knowledge, learning progress, and learning preferences. This study reviews various implementation of adaptive feedback, based on the four adaptation characteristics: means, target, goal, and strategy. This review focuses on 20 different implementations of feedback in a computer-based learning environment, ranging from multimedia web-based intelligent tutoring systems, dialog-based intelligent tutoring systems, web-based intelligent e-learning systems, adaptive hypermedia systems, and adaptive learning environment. The main objective of the review is to compare computer-based learning environments according to their implementation of feedback and to identify open research questions in adaptive feedback implementations. The review resulted in categorizing these feedback implementations based on the students' information used for providing feedback, the aspect of the domain or pedagogical knowledge that is adapted to provide feedback based on the students' characteristics, the pedagogical reason for providing feedback, and the steps taken to provide feedback with or without students' participation. Other information such as the common adaptive feedback means, goals, and implementation techniques are identified. This review reveals a distinct relationship between the characteristics of feedback, features of adaptive feedback, and computer-based learning models. Other information such as the common adaptive feedback means, goals, implementation techniques, and open research questions are identified.

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“…Current educational situations necessitate developing and implementing alternative strategies to replace the previous functions of learning evaluation settings due to COVID-19. The integration of artificially intelligent components into educational systems greatly helps to efficiently provide meaningful information for evaluation and learning [39][40][41]. On the one hand, adaptive testing technology offers an efficient and reliable personalized evaluation system.…”

Section: Discussionmentioning

confidence: 99%

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

Choi

McClenen

2020

Applied Sciences

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Online formative assessments in e-learning systems are increasingly of interest in the field of education. While substantial research into the model and item design aspects of formative assessment has been conducted, few software systems embodied with a psychometric model have been proposed to allow us to adaptively implement formative assessments. This study aimed to develop an adaptive formative assessment system, called computerized formative adaptive testing (CAFT) by using artificial intelligence methods based on computerized adaptive testing (CAT) and Bayesian networks as learning analytics. CAFT can adaptively administer personalized formative assessment to a learner by dynamically selecting appropriate items and tests aligned with the learner’s ability. Forty items in an item bank were evaluated by 410 learners, moreover, 1000 learners were recruited for a simulation study and 120 learners were enrolled to evaluate the efficiency, validity, and reliability of CAFT in an application study. The results showed that, through CAFT, learners can adaptively take item s and tests in order to receive personalized diagnostic feedback about their learning progression. Consequently, this study highlights that a learning management system which integrates CAT as an artificially intelligent component is an efficient educational evaluation tool for a remote personalized learning service.

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A Classification of Adaptive Feedback in Educational Systems for Programming

Cited by 31 publications

References 38 publications

Designing and Developing Videos for Online Learning: A Seven-Principle Model

Designing and Developing Videos for Online Learning: A Seven-Principle Model

Adaptive feedback in computer-based learning environments: a review

Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks

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