Learning linkages: Integrating data streams of multiple modalities and timescales.
Increasingly, student work is being conducted on computers and online, producing vast amounts of learning‐related data. The educational analytics fields have produced many insights about learning based solely on tutoring systems' automatically logged data, or "log data." But log data leave out impor...
| Publicado en: | Journal of Computer Assisted Learning Vol. 35; no. 1; pp. 99 - 110 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134200916&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134200916 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2019 vid: 35 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 134200916 134200916 134200916 10.1111/jcal.12315 134200916 ppf: 99 ppct: 11 formats: tig: atl: Learning linkages: Integrating data streams of multiple modalities and timescales. aug: au: Liu, Ran Stamper, John Davenport, Jodi Crossley, Scott McNamara, Danielle Nzinga, Kalonji Sherin, Bruce affil: Human‐Computer Interaction Institute, Carnegie Mellon University, Pittsburgh Pennsylvania sug: subj: Education, Non-Traditional Teaching Methods Learning Collaboration Educational Technology Utilization Educational Technology Equipment and Supplies Human Students, Elementary Videorecording Audiorecording Learning Methods Data Analytics Natural Language Processing Empirical Research Quantitative Studies Qualitative Studies Webcasts Male Female Data Analysis, Statistical T-Tests Pretest-Posttest Design Descriptive Statistics Male Female ab: Increasingly, student work is being conducted on computers and online, producing vast amounts of learning‐related data. The educational analytics fields have produced many insights about learning based solely on tutoring systems' automatically logged data, or "log data." But log data leave out important contextual information about the learning experience. For example, a student working at a computer might be working independently with few outside influences. Alternatively, he or she might be in a lively classroom, with other students around, talking and offering suggestions. Tools that capture these other experiences have potential to augment and complement log data. However, the collection of rich, multimodal data streams and the increased complexity and heterogeneity in the resulting data pose many challenges to researchers. Here, we present two empirical studies that take advantage of multimodal data sources to enrich our understanding of student learning. We leverage and extend quantitative models of student learning to incorporate insights derived jointly from data collected in multiple modalities (log data, video, and high‐fidelity audio) and contexts (individual vs. collaborative classroom learning). We discuss the unique benefits of multimodal data and present methods that take advantage of such benefits while easing the burden on researchers' time and effort. Lay Description: What is already known about this topic: When students use education software in the classroom, the software automatically logs their entries, clicks, and other submitted activity.Researchers can use this detailed software‐logged data to infer information about students' learning processes.However, software cannot log some important events that happen during students' learning experience.One example is students' interactions with others while they use the software.What this paper adds: We collected video and audio while students used educational software in the classroom, either individually or working with a partner.The audio and video provided information on students' learning processes and contexts that the software‐logged data could not provide.We were able to gain an improved understanding of students' learning processes through the audio and video data.Implications for practice and/or policy: Students who engage in productive struggle on difficult problems rather than taking an easier or more direct path to the answer show improved learning trajectories following those problems.Features of the language students use with each other when collaborating on math learning predict their math performance. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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