Modeling the Semantic Structure of Textually Derived Learning Content and its Impact on Recipients' Response States

Muñoz, David A.; Tucker, Conrad S.

doi:10.1115/1.4032398

Cited by 10 publications

(13 citation statements)

References 37 publications

(17 reference statements)

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“…W 1 and W 2 are a set of top t 1 frequent terms in MOOC transcriptions and a set of top t 2 frequent terms in student feedback data in descending order, respectively, as seen in Equation . Equation shows the weighted adjacency matrix A 1 that expresses co‐occurrence between top t 1 frequent terms in MOOC transcriptions .

W_{1} = [w_{11}, w_{12}, \dots, w_{1 k_{1} - 1}, w_{1 k_{1}}], W_{2} = [w_{21}, w_{22}, \dots, w_{2 k_{2} - 1}, w_{2 k_{2}}]

A_{1} = true[\begin{matrix} center \\ center - & center o_{112} & center o_{113} & center \dots & center o_{11 t_{1}} \\ center o_{121} & center - & center o_{123} & center \dots & center o_{12 t_{1}} \\ center o_{131} & center o_{132} & center - & center \dots & center o_{13 t_{1}} \\ center \dots & center \dots & center \dots & center - & center \dots \\ center o_{1 t_{1} 1} & center o_{1 t_{1} 2} & center o_{1 t_{1} 3} & center \dots & center - \end{matrix} true]

where o 1 ij represents the frequency in which both term w 1 i and w 1 j co‐occur in the same document.…”

Section: Methodsmentioning

confidence: 99%

“…The average geodesic distance of student feedback data (i.e., L 2 ) is defined in the same manner. The geodesic distance evaluates the cohesion of the semantic network (i.e., how close the ideas of MOOC transcripts or student feedback are developed) .

L_{1} = \frac{{true \sum}_{n = 1}^{| T_{1} |} {true \sum}_{m = 1, m \neq n}^{| T_{1} |} d_{1 m n}}{| T_{1} | \cdot (| T_{1} | - 1)}

…”

Section: Methodsmentioning

confidence: 99%

“…W 1 and W 2 are a set of top t 1 frequent terms in MOOC transcriptions and a set of top t 2 frequent terms in student feedback data in descending order, respectively, as seen in Equation 1. Equation 2 shows the weighted adjacency matrix A 1 that expresses co-occurrence between top t 1 frequent terms in MOOC transcriptions [34].…”

Section: Term Frequency and Semantic Networkmentioning

confidence: 99%

See 2 more Smart Citations

A semantic network model for measuring engagement and performance in online learning platforms

Lim

Tucker

Jablokow

et al. 2018

Comp Applic In Engineering

Self Cite

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Due to the increasing global availability of the internet, online learning platforms such as Massive Open Online Courses (MOOCs), have become a new paradigm for distance learning in engineering education. While interactions between instructors and students are readily observable in a physical classroom environment, monitoring student engagement is challenging in MOOCs. Monitoring student engagement and measuring its impact on student performance are important for MOOC instructors, who are focused on improving the quality of their courses. The authors of this work present a semantic network model for measuring the different word associations between instructors and students in order to measure student engagement in MOOCs. Correlation analysis is then performed for identifying how student engagement in MOOCs affect student performance. Real‐world MOOC transcripts and MOOC discussion forum data are used to evaluate the effectiveness of this research.

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W_{1} = [w_{11}, w_{12}, \dots, w_{1 k_{1} - 1}, w_{1 k_{1}}], W_{2} = [w_{21}, w_{22}, \dots, w_{2 k_{2} - 1}, w_{2 k_{2}}]

A_{1} = true[\begin{matrix} center \\ center - & center o_{112} & center o_{113} & center \dots & center o_{11 t_{1}} \\ center o_{121} & center - & center o_{123} & center \dots & center o_{12 t_{1}} \\ center o_{131} & center o_{132} & center - & center \dots & center o_{13 t_{1}} \\ center \dots & center \dots & center \dots & center - & center \dots \\ center o_{1 t_{1} 1} & center o_{1 t_{1} 2} & center o_{1 t_{1} 3} & center \dots & center - \end{matrix} true]

where o 1 ij represents the frequency in which both term w 1 i and w 1 j co‐occur in the same document.…”

Section: Methodsmentioning

confidence: 99%

L_{1} = \frac{{true \sum}_{n = 1}^{| T_{1} |} {true \sum}_{m = 1, m \neq n}^{| T_{1} |} d_{1 m n}}{| T_{1} | \cdot (| T_{1} | - 1)}

…”

Section: Methodsmentioning

confidence: 99%

“…W 1 and W 2 are a set of top t 1 frequent terms in MOOC transcriptions and a set of top t 2 frequent terms in student feedback data in descending order, respectively, as seen in Equation 1. Equation 2 shows the weighted adjacency matrix A 1 that expresses co-occurrence between top t 1 frequent terms in MOOC transcriptions [34].…”

Section: Term Frequency and Semantic Networkmentioning

confidence: 99%

See 1 more Smart Citation

A semantic network model for measuring engagement and performance in online learning platforms

Lim

Tucker

Jablokow

et al. 2018

Comp Applic In Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although there is still ongoing research on the correlation between semantic structure of the usergenerated textual information and users' actual effective states [60], the usefulness of the consumergenerated information has been already proved in the literature [61], [62]. Quite recently, some research has focused on extractingknowledge from the textual content of this information.…”

Section: Text Analyticsmentioning

confidence: 99%

Mining consumer experiences of repairing electronics: Product design insights and business lessons learned

Mashhadi

Esmaeilian

Cade³

et al. 2016

Journal of Cleaner Production

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Despite various efforts to shed light on thedifferent aspects of repair and maintenanceprocesses, almost all of our (our entire) understanding ofthe repair practices done on consumer electronics comes from either themanufactures or professional repair experts. There is a lack of systematic, well-documented studies of repair practices by unprofessional individual consumers. Understanding the factors contributing to unprofessional repair practices is a necessity to lengthen the life span of the product and to promote repair as an eco-behavior among individual consumers. We have investigated 4210 break and fix narratives reported by consumers of electronic devices in a survey conducted by iFixit.com-a wiki-based website for repair manuals-in order to apperceive the most common failures, repair practices and challenges that individual users face in their interactive experiences with product repair. A comprehensive text mining set up has been applied to extract the most frequent product break stories and their causes of failure. Regression analyses have been employed to examine the possible links between consumer experiences of repairing electronics and their future purchase behaviors.The results of analyses indicate that in addition to the consumers' attitude toward repair, various product design features offer different levels of repair convenience, which may eventually impact the consumer's future purchase decisions.

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“…In an attempt to address these issues, semiautomated/automated technologies have been developed to capture designers' internal representations using text [11], speech [12] or body language [13]. For example, individuals' internal representation can be captured and mined through textual data in order to model individuals' responses to external stimuli [14]. However, analyzing designers' internal representations using text may be impractical in a design workshop environment because it may interfere with the required task at hand.…”

Section: Introductionmentioning

confidence: 99%

Automatic Facial Feature Extraction for Predicting Designers' Comfort With Engineering Equipment During Prototype Creation

Bezawada

Gray

et al. 2017

Journal of Mechanical Design

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Designers frequently utilize engineering equipment to create physical prototypes during the iterative concept generation and prototyping phases of design. Currently, evaluating designers' efficiency during prototype creation is a manual process that either involves observational or survey based approaches. Real-time feedback when using engineering equipment has the potential to enhance designers' efficiency or mitigate potential injuries that may result from incorrect use of equipment. Toward an automated approach to addressing these challenges, the authors of this work test the hypotheses that (i) there exists a difference in designers' comfort levels before and after they use a piece of engineering prototyping equipment and (ii) a machine learning model predicts the level of comfort a designer has while using engineering prototyping equipment with accuracies greater than random chance. It has been shown that the level of comfort that an individual has while completing a task impacts their performance. The authors investigate whether automatic tracking of designers' facial expressions during prototype creation predicts their level of comfort. A study, involving 37 participants using various engineering equipment, is used to validate the approach. The support vector machine (SVM) regression model yielded a range of R squared values from 0.82 to 0.86 for an equipment-specific model. A general model built to predict comfort level across all engineering equipment yielded an R squared value of 0.68. This work has the potential to transform the manner in which design teams utilize engineering equipment toward more efficient concept generation and prototype creation processes.

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Modeling the Semantic Structure of Textually Derived Learning Content and its Impact on Recipients' Response States

Cited by 10 publications

References 37 publications

A semantic network model for measuring engagement and performance in online learning platforms

A semantic network model for measuring engagement and performance in online learning platforms

Mining consumer experiences of repairing electronics: Product design insights and business lessons learned

Automatic Facial Feature Extraction for Predicting Designers' Comfort With Engineering Equipment During Prototype Creation

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