Integrating Programming Learning Analytics Across Physical and Digital Space

In this work, we study students' learning effectiveness through their use of a homegrown innovative educational technology, Web Programming Grading Assistant (WPGA), which facilitates grading and feedback delivery of paper-based assessments. We designed a lab study and a classroom study from a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on emerging topics in computing 2020-01, Vol.8 (1), p.206-217
Hauptverfasser: Hsiao, I-Han, Huang, Po-Kai, Murphy, Hannah
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 217
container_issue 1
container_start_page 206
container_title IEEE transactions on emerging topics in computing
container_volume 8
creator Hsiao, I-Han
Huang, Po-Kai
Murphy, Hannah
description In this work, we study students' learning effectiveness through their use of a homegrown innovative educational technology, Web Programming Grading Assistant (WPGA), which facilitates grading and feedback delivery of paper-based assessments. We designed a lab study and a classroom study from a lower-division blended-instruction computer science course. We evaluated a partial credit assignment algorithm. We tracked and modeled students' learning behaviors through their use of WPGA. Results showed that students demonstrated an effort and desire to review assessments regardless of if they were graded for academic performance or for attendance. Diligent students achieved higher exam scores on average and were found to review their exams and the correct questions frequently. Additionally, student cohorts exhibited similar initial reviewing patterns, but different in-depth reviewing and reflecting strategies. Ultimately, the work contributes to multidimensional learning analytics aggregation across the physical and cybersphere.
doi_str_mv 10.1109/TETC.2017.2701201
format Article
fullrecord <record><control><sourceid>proquest_ESBDL</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TETC_2017_2701201</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7918521</ieee_id><sourcerecordid>2374696022</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-e161c1d825569dde9b9a951b05463befa221f4f9aaa3df843769915d7b6e7f993</originalsourceid><addsrcrecordid>eNpNkFFLwzAUhYMoOOZ-gPhS8LkzN2mS5rFMnYOCA-dzSNtkZmxtTbqH_XtTN8T7cr-Hcy7nHoTuAc8BsHzavGwWc4JBzInAEOEKTQjwPOWC4et_fItmIexwnBy45GKCylU7mK3Xg2u3ydp3EQ-HkUujfTtC0er9aXB1SIradyEk669TcLXeJ7ptkme3dUPkj17X5g7dWL0PZnbZU_T5GqO9peX7crUoyrSmTA6pAQ41NDlhjMumMbKSWjKoMMs4rYzVhIDNrNRa08bmGRVcSmCNqLgRVko6RY_nu73vvo8mDGrXHX3MGRShIoufYUKiCs6q39jeWNV7d9D-pACrsTc19qbG3tSlt-h5OHucMeZPLyTkjAD9ASeEaJY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2374696022</pqid></control><display><type>article</type><title>Integrating Programming Learning Analytics Across Physical and Digital Space</title><source>IEEE Open Access Journals</source><creator>Hsiao, I-Han ; Huang, Po-Kai ; Murphy, Hannah</creator><creatorcontrib>Hsiao, I-Han ; Huang, Po-Kai ; Murphy, Hannah</creatorcontrib><description>In this work, we study students' learning effectiveness through their use of a homegrown innovative educational technology, Web Programming Grading Assistant (WPGA), which facilitates grading and feedback delivery of paper-based assessments. We designed a lab study and a classroom study from a lower-division blended-instruction computer science course. We evaluated a partial credit assignment algorithm. We tracked and modeled students' learning behaviors through their use of WPGA. Results showed that students demonstrated an effort and desire to review assessments regardless of if they were graded for academic performance or for attendance. Diligent students achieved higher exam scores on average and were found to review their exams and the correct questions frequently. Additionally, student cohorts exhibited similar initial reviewing patterns, but different in-depth reviewing and reflecting strategies. Ultimately, the work contributes to multidimensional learning analytics aggregation across the physical and cybersphere.</description><identifier>ISSN: 2168-6750</identifier><identifier>EISSN: 2168-6750</identifier><identifier>DOI: 10.1109/TETC.2017.2701201</identifier><identifier>CODEN: ITETBT</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Assessments ; behavior modeling ; blended instruction classes ; Educational technology ; Learning analytics ; Machine learning ; multimodal analytics ; orchestration technology ; programming learning ; Programming profession ; Reflection ; Reviewing ; Students ; Technological innovation ; Timing</subject><ispartof>IEEE transactions on emerging topics in computing, 2020-01, Vol.8 (1), p.206-217</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-e161c1d825569dde9b9a951b05463befa221f4f9aaa3df843769915d7b6e7f993</citedby><cites>FETCH-LOGICAL-c359t-e161c1d825569dde9b9a951b05463befa221f4f9aaa3df843769915d7b6e7f993</cites><orcidid>0000-0002-1888-3951</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7918521$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27633,27924,27925,54758,54933</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7918521$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hsiao, I-Han</creatorcontrib><creatorcontrib>Huang, Po-Kai</creatorcontrib><creatorcontrib>Murphy, Hannah</creatorcontrib><title>Integrating Programming Learning Analytics Across Physical and Digital Space</title><title>IEEE transactions on emerging topics in computing</title><addtitle>TETC</addtitle><description>In this work, we study students' learning effectiveness through their use of a homegrown innovative educational technology, Web Programming Grading Assistant (WPGA), which facilitates grading and feedback delivery of paper-based assessments. We designed a lab study and a classroom study from a lower-division blended-instruction computer science course. We evaluated a partial credit assignment algorithm. We tracked and modeled students' learning behaviors through their use of WPGA. Results showed that students demonstrated an effort and desire to review assessments regardless of if they were graded for academic performance or for attendance. Diligent students achieved higher exam scores on average and were found to review their exams and the correct questions frequently. Additionally, student cohorts exhibited similar initial reviewing patterns, but different in-depth reviewing and reflecting strategies. Ultimately, the work contributes to multidimensional learning analytics aggregation across the physical and cybersphere.</description><subject>Algorithms</subject><subject>Assessments</subject><subject>behavior modeling</subject><subject>blended instruction classes</subject><subject>Educational technology</subject><subject>Learning analytics</subject><subject>Machine learning</subject><subject>multimodal analytics</subject><subject>orchestration technology</subject><subject>programming learning</subject><subject>Programming profession</subject><subject>Reflection</subject><subject>Reviewing</subject><subject>Students</subject><subject>Technological innovation</subject><subject>Timing</subject><issn>2168-6750</issn><issn>2168-6750</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkFFLwzAUhYMoOOZ-gPhS8LkzN2mS5rFMnYOCA-dzSNtkZmxtTbqH_XtTN8T7cr-Hcy7nHoTuAc8BsHzavGwWc4JBzInAEOEKTQjwPOWC4et_fItmIexwnBy45GKCylU7mK3Xg2u3ydp3EQ-HkUujfTtC0er9aXB1SIradyEk669TcLXeJ7ptkme3dUPkj17X5g7dWL0PZnbZU_T5GqO9peX7crUoyrSmTA6pAQ41NDlhjMumMbKSWjKoMMs4rYzVhIDNrNRa08bmGRVcSmCNqLgRVko6RY_nu73vvo8mDGrXHX3MGRShIoufYUKiCs6q39jeWNV7d9D-pACrsTc19qbG3tSlt-h5OHucMeZPLyTkjAD9ASeEaJY</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Hsiao, I-Han</creator><creator>Huang, Po-Kai</creator><creator>Murphy, Hannah</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-1888-3951</orcidid></search><sort><creationdate>202001</creationdate><title>Integrating Programming Learning Analytics Across Physical and Digital Space</title><author>Hsiao, I-Han ; Huang, Po-Kai ; Murphy, Hannah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-e161c1d825569dde9b9a951b05463befa221f4f9aaa3df843769915d7b6e7f993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Assessments</topic><topic>behavior modeling</topic><topic>blended instruction classes</topic><topic>Educational technology</topic><topic>Learning analytics</topic><topic>Machine learning</topic><topic>multimodal analytics</topic><topic>orchestration technology</topic><topic>programming learning</topic><topic>Programming profession</topic><topic>Reflection</topic><topic>Reviewing</topic><topic>Students</topic><topic>Technological innovation</topic><topic>Timing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hsiao, I-Han</creatorcontrib><creatorcontrib>Huang, Po-Kai</creatorcontrib><creatorcontrib>Murphy, Hannah</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on emerging topics in computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hsiao, I-Han</au><au>Huang, Po-Kai</au><au>Murphy, Hannah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrating Programming Learning Analytics Across Physical and Digital Space</atitle><jtitle>IEEE transactions on emerging topics in computing</jtitle><stitle>TETC</stitle><date>2020-01</date><risdate>2020</risdate><volume>8</volume><issue>1</issue><spage>206</spage><epage>217</epage><pages>206-217</pages><issn>2168-6750</issn><eissn>2168-6750</eissn><coden>ITETBT</coden><abstract>In this work, we study students' learning effectiveness through their use of a homegrown innovative educational technology, Web Programming Grading Assistant (WPGA), which facilitates grading and feedback delivery of paper-based assessments. We designed a lab study and a classroom study from a lower-division blended-instruction computer science course. We evaluated a partial credit assignment algorithm. We tracked and modeled students' learning behaviors through their use of WPGA. Results showed that students demonstrated an effort and desire to review assessments regardless of if they were graded for academic performance or for attendance. Diligent students achieved higher exam scores on average and were found to review their exams and the correct questions frequently. Additionally, student cohorts exhibited similar initial reviewing patterns, but different in-depth reviewing and reflecting strategies. Ultimately, the work contributes to multidimensional learning analytics aggregation across the physical and cybersphere.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TETC.2017.2701201</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1888-3951</orcidid></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2168-6750
ispartof IEEE transactions on emerging topics in computing, 2020-01, Vol.8 (1), p.206-217
issn 2168-6750
2168-6750
language eng
recordid cdi_crossref_primary_10_1109_TETC_2017_2701201
source IEEE Open Access Journals
subjects Algorithms
Assessments
behavior modeling
blended instruction classes
Educational technology
Learning analytics
Machine learning
multimodal analytics
orchestration technology
programming learning
Programming profession
Reflection
Reviewing
Students
Technological innovation
Timing
title Integrating Programming Learning Analytics Across Physical and Digital Space
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T22%3A17%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ESBDL&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Integrating%20Programming%20Learning%20Analytics%20Across%20Physical%20and%20Digital%20Space&rft.jtitle=IEEE%20transactions%20on%20emerging%20topics%20in%20computing&rft.au=Hsiao,%20I-Han&rft.date=2020-01&rft.volume=8&rft.issue=1&rft.spage=206&rft.epage=217&rft.pages=206-217&rft.issn=2168-6750&rft.eissn=2168-6750&rft.coden=ITETBT&rft_id=info:doi/10.1109/TETC.2017.2701201&rft_dat=%3Cproquest_ESBDL%3E2374696022%3C/proquest_ESBDL%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2374696022&rft_id=info:pmid/&rft_ieee_id=7918521&rfr_iscdi=true