Exploring feedback and student characteristics relevant for personalizing feedback strategies

Narciss, Susanne; Sosnovsky, Sergey; Schnaubert, Lenka; Andrès, Eric; Eichelmann, Anja; Goguadze, George; Melis, Erica

doi:10.1016/j.compedu.2013.09.011

Cited by 178 publications

(115 citation statements)

References 34 publications

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“…Tsai, Tsai, and Lin. [36] developed a TRIS-Q system involving formative assessment and different feedback types to enable students to perform self-assessment when desired. Lin et al [37] proposed the Across-Unit Diagnostic Feedback System (AUDFS) using an across-unit diagnostic feedback mechanism, which provided feedback that can be used to recommend remedial learning paths for students, and inform the students of the priorities of the paths to understand which weak units and concepts within a unit should be remedied first.…”

Section: 2mentioning

confidence: 99%

Investigating the Effectiveness of an Interactive IRF-Based English Grammar Learning System

2017

Int. J. Emerg. Technol. Learn.

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Abstract-Computer-supported language learning has the potential to promote relevant work in support of instant, interactive language discourse that fulfils educational purposes and fosters individual language learning. This study presents an interactive IRF-based English grammar learning assistant system (EGLA) that aims to improve students' grammatical knowledge and correct their grammar errors and misspellings if they have any. A quasi-experimental design is adopted to examine its effects on an English grammar achievement test for three classes of junior high school students. These three classes were assigned to the experimental group and control groups A and B. The experimental group used EGLA that harnessed IRF moves, control group A used EGLA but without IRF moves, whereas control group B received an IRF-based English grammar discourse from an English tutor in the classroom. The results demonstrated that the experimental group was significantly better than control group A, but was not significantly different from control group B. The results are discussed, and directions for further investigation are provided.

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Section: 2mentioning

confidence: 99%

Investigating the Effectiveness of an Interactive IRF-Based English Grammar Learning System

2017

Int. J. Emerg. Technol. Learn.

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“…Computer-based learning environments have the capability to generate a variety of feedback mechanisms to inform learners about task performance (Corbalan, Paas, & Cuypers, 2010;Hsieh, & O'Neil, 2002;Narciss, et al, 2013). The most basic form of feedback is knowledge of results (KOR), or corrective feedback, which simply reports whether a studentgenerated response is correct or incorrect (Clark & Dwyer, 1998;Mason & Bruning, 2001).…”

Section: Introductionmentioning

confidence: 99%

Transitional feedback schedules during computer-based problem-solving practice

Johnson

Reisslein

2015

Computers & Education

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Feedback has a strong influence on effective learning from computer-based instruction. Prior research on feedback in computer-based instruction has mainly focused on static feedback schedules that employ the same feedback schedule through an instructional session. This study examined transitional feedback schedules in computer-based multimedia instruction on procedural problem-solving in electrical circuit analysis. Specifically, we compared two transitional feedback schedules: the TFS-P schedule switched from initial feedback after each problem step to feedback after a complete problem at later learning states; the TFP-S schedule transitioned from feedback after a complete problem to feedback after each problem step. As control conditions, we also considered two static feedback schedules, namely providing feedback after each practice problem-solving step (SFS) or providing feedback after attempting a complete multi-step practice problem (SFP). Results indicate that the static stepwise (SFS) and transitional stepwise to problem (TFS-P) feedback produce higher problem solving near-transfer post-test performance than static problem (SFP) and transitional problem to step (TFP-S) feedback. Also, TFS-P resulted in higher ratings of program liking and feedback helpfulness than TFP-S. Overall, the study results indicate benefits of maintaining high feedback frequency (SFS) and reducing feedback frequency (TFS-P) compared to low feedback frequency (SFP) or increasing feedback frequency (TFP-S) as novice learners acquire engineering problem solving skills.

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“…The student model and its adaptation algorithm determine the type of feedback as well as the feedback strategy. This notion of adaptive feedback is also referred to as "personalized feedback" [12]. The representative educational systems for programming that support adaptive feedback in this sense include efforts that gradually provide appropriate feedback information adapting to the students' needs, e.g., [12], and systems that provides feedback based on students' individual knowledge level, e.g., [14] or gender, e.g., [15].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, these are many individual factors that may influence how feedback is processed by each learner. Consequently, a variety of individual factors can be used to support the design of personalized feedback strategies and implementation of AESs (Adaptive Educational Systems) which can deliver feedback messages that are tailored to the characteristics of an individual learner or a category of learners" ( [12], p. 59). In this sense, adaptive feedback is generated by an adaptive educational system that requires a student model to represent the student's characteristics (e.g., knowledge, meta-cognitive skills, affective state, learning strategies and styles) and the student model serves to distinguish the level among different students [13].…”

Section: Introductionmentioning

confidence: 99%

A Classification of Adaptive Feedback in Educational Systems for Programming

2016

Systems

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Over the last three decades, many educational systems for programming have been developed to support learning/teaching programming. In this paper, feedback types that are supported by existing educational systems for programming are classified. In order to be able to provide feedback, educational systems for programming deployed various approaches to analyzing students' programs. This paper identifies analysis approaches for programs and introduces a classification for adaptive feedback supported by educational systems for programming. The classification of feedback is the contribution of this paper.

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Exploring feedback and student characteristics relevant for personalizing feedback strategies

Cited by 178 publications

References 34 publications

Investigating the Effectiveness of an Interactive IRF-Based English Grammar Learning System

Investigating the Effectiveness of an Interactive IRF-Based English Grammar Learning System

Transitional feedback schedules during computer-based problem-solving practice

A Classification of Adaptive Feedback in Educational Systems for Programming

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