Designing and developing a language environment for second language writers

Knutsson, Ola; Pargman, Teresa Cerratto; Eklundh, Kerstin Severinson; Westlund, Stefan

doi:10.1016/j.compedu.2006.01.005

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Referring to the principles noted in Jonassen [45] and Shute [23], learning feedback should show more information, instead of just simple results (correct/incorrect) or correct answers, as learners need to be told where their problem-solving process went wrong and coached from that point onward. Moreover, if the feedback is too long or too complex it could decrease students' attention, and so a hierarchical approach to feedback was used in this study.…”

Section: Irf-based Interactive Dialogue Modulementioning

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: Irf-based Interactive Dialogue Modulementioning

confidence: 99%

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

2017

Int. J. Emerg. Technol. Learn.

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“…In their work developing a Swedish grammar checker, Knutsson et al studied adoption practices of their grammar checker by non-native speakers [12,13]. Based on their study, they developed a set of guidelines for the design of linguistic tools for NNSs.…”

Section: L2 Tool Design Guidelinesmentioning

confidence: 99%

“…For end-user linguistic tools dealing with fuzzily-defined linguistic conventions (such as collocation preferences), the properties of model-based approaches may be problematic. In particular, using the desiderata of Knutsson et al [13] and Vernon [20], models may serve to decrease the transparency of the system, making it difficult for users to form a mental model that can account for failures of the system, such as false positives and poor suggested corrections. As these techniques have not been realized in end-user tools, these remain important open questions to investigate.…”

Section: L2 Error Detection Toolsmentioning

confidence: 99%

“…In this paper, we are most concerned with collocation errors, an L2 error that has received little attention in the past. To aid NNSs in detecting and correcting errors, linguistic tools for NNS should provide real-time feedback, information to support reasoning about errors, a high degree of transparency with respect to the tool's functionality and capabilities, and little reliance on specialized terminology [13,20]. We turn now to a system we built to detect collocation errors while following these general guidelines.…”

Section: Summarizing L2 Learner Needsmentioning

confidence: 99%

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Is the sky pure today? AwkChecker

Park

Lank

Poupart

et al. 2008

Proceedings of the 21st Annual ACM Symposium on User Interface Software and Technology

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Collocation preferences represent the commonly used expressions, idioms, and word pairings of a language. Because collocation preferences arise from consensus usage, rather than a set of well-defined rules, they must be learned on a case-by-case basis, making them particularly challenging for non-native speakers of a language. To assist non-native speakers with these parts of a language, we developed AwkChecker, the first end-user tool geared toward helping non-native speakers detect and correct collocation errors in their writing. As a user writes, AwkChecker automatically flags collocation errors and suggests replacement expressions that correspond more closely to consensus usage. These suggestions include example usage to help users choose the best candidate. We describe AwkChecker's interface, its novel methods for detecting collocation errors and suggesting alternatives, and an early study of its use by non-native English speakers at our institution. Collectively, these contributions advance the state of the art in writing aids for non-native speakers.

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“…The unique combination highlights the rich interaction between users and ML systems to promote the users' understanding of the system and to improve the high accuracy of ML systems [13][14]. The human-computer collaboration via on-the-spot interactions is shown as a promising direction in SLL domain because ML systems can enhance the effectiveness and users can simultaneously share intelligence [15][16].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Second Language Learning: Effects on Syntactic Processing in Garden-Path Sentences

2010

2010 International Conference on Computational Intelligence and Software Engineering

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With the rapid development of educational technologies, machine learning (ML) based second language learning (SLL) attracts the attention of many scholars from computational linguistics. Garden path (GP) sentence is a special sentence structure in which processing breakdown and backtracking are involved in the machine decoding. Faced with GP sentence, learners have to make original misinterpretation linger even after reanalysis has occurred, and have to face the fact that initial interpretations of GP sentence are often incorrect and do not correspond to the final output. On the basis of computational skills of N-S flowchart, recursive transition network, and well-formed substring table, this paper highlights the effects on syntactic processing in garden-path sentences when context-free grammar (CFG) driven ML system is introduced. There are two main conclusions to be drawn from the above discussion. Firstly, CFG-driven ML system can help SLL learners better understand the GP sentences in second language even no rich lingual environment is provided. Secondly, the hybrid knowledge, e.g. grammatical, cognitive and semantic information, needs to be integrated into ML system to help SLL learners achieve the "good enough" results in decoding GP sentences.

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Designing and developing a language environment for second language writers

Cited by 16 publications

References 13 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

Is the sky pure today? AwkChecker

Machine Learning for Second Language Learning: Effects on Syntactic Processing in Garden-Path Sentences

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