PIPE: Predicting Logical Programming Errors in Programming Exercises
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| Název: | PIPE: Predicting Logical Programming Errors in Programming Exercises |
|---|---|
| Jazyk: | English |
| Autoři: | Miao, Dezhuang, Dong, Yu, Lu, Xuesong |
| Zdroj: | International Educational Data Mining Society. 2020. |
| Dostupnost: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 7 |
| Datum vydání: | 2020 |
| Druh dokumentu: | Speeches/Meeting Papers Reports - Descriptive |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Programming, Prediction, Error Patterns, Models, Educational Technology, Technology Uses in Education, Feedback (Response), Undergraduate Students |
| Abstrakt: | In colleges, programming is increasingly becoming a general education course of almost all STEM majors as well as some art majors, resulting in an emerging demand for scalable programming education. To support scalable education, teaching activities such as grading and feedback have to be automated. Recently, online judge systems have been extensively used for programming training, because they are able to automatically evaluate the correctness of programs in real time and thereby make grading work scalable. However, existing online judge systems lack of the ability to give effective feedback on logical programming errors. As such, instructors and teaching assistants are still overwhelmed by the work of helping students fix programs, especially for those novice students. To tackle the challenge, we develop "PIPE," a deep learning model that is able to "P"redict log"I"cal "P"rogramming "E"rrors in student programs. The model seamlessly integrates a representation learning model for obtaining the latent feature of a program and a multi-label classification model for predicting the error types in the program, thereby allowing end-to-end learning and prediction. We use the C programs submitted in our online judge system to train PIPE, and demonstrate its superior performance over the baseline models. We use PIPE to implement the error-feedback feature in our online judge system and enable automated feedback on logical programming errors to the students. [For the full proceedings, see ED607784.] |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Přístupové číslo: | ED607998 |
| Databáze: | ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: PIPE: Predicting Logical Programming Errors in Programming Exercises – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Miao%2C+Dezhuang%22">Miao, Dezhuang</searchLink><br /><searchLink fieldCode="AR" term="%22Dong%2C+Yu%22">Dong, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Lu%2C+Xuesong%22">Lu, Xuesong</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2020. – Name: Avail Label: Availability Group: Avail Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 7 – Name: DatePubCY Label: Publication Date Group: Date Data: 2020 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Descriptive – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Programming%22">Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Patterns%22">Error Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In colleges, programming is increasingly becoming a general education course of almost all STEM majors as well as some art majors, resulting in an emerging demand for scalable programming education. To support scalable education, teaching activities such as grading and feedback have to be automated. Recently, online judge systems have been extensively used for programming training, because they are able to automatically evaluate the correctness of programs in real time and thereby make grading work scalable. However, existing online judge systems lack of the ability to give effective feedback on logical programming errors. As such, instructors and teaching assistants are still overwhelmed by the work of helping students fix programs, especially for those novice students. To tackle the challenge, we develop "PIPE," a deep learning model that is able to "P"redict log"I"cal "P"rogramming "E"rrors in student programs. The model seamlessly integrates a representation learning model for obtaining the latent feature of a program and a multi-label classification model for predicting the error types in the program, thereby allowing end-to-end learning and prediction. We use the C programs submitted in our online judge system to train PIPE, and demonstrate its superior performance over the baseline models. We use PIPE to implement the error-feedback feature in our online judge system and enable automated feedback on logical programming errors to the students. [For the full proceedings, see ED607784.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2020 – Name: AN Label: Accession Number Group: ID Data: ED607998 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 7 Subjects: – SubjectFull: Programming Type: general – SubjectFull: Prediction Type: general – SubjectFull: Error Patterns Type: general – SubjectFull: Models Type: general – SubjectFull: Educational Technology Type: general – SubjectFull: Technology Uses in Education Type: general – SubjectFull: Feedback (Response) Type: general – SubjectFull: Undergraduate Students Type: general Titles: – TitleFull: PIPE: Predicting Logical Programming Errors in Programming Exercises Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Miao, Dezhuang – PersonEntity: Name: NameFull: Dong, Yu – PersonEntity: Name: NameFull: Lu, Xuesong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Type: published Y: 2020 Titles: – TitleFull: International Educational Data Mining Society Type: main |
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