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

Lim, Sunghoon; Tucker, Conrad S.; Jablokow, Kathryn; Pursel, Bart

doi:10.1002/cae.22033

Cited by 20 publications

(13 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Semantic network analysis can identify the inherent meaning of the context that has not been revealed. Moreover, it is possible to understand the degree of influence of a particular word through frequency and cluster analysis of words and to analyze how a particular word affects the relationships between groups [40,41]. Semantic network analysis, as a method of quantitative textual analysis, provides an impressive theoretical and methodological foundation with which to describe the semantic nature of the online review [42].…”

Section: Text Mining and Semantic Network Analysismentioning

confidence: 99%

Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

Ban

Choi

et al. 2019

Sustainability

View full text Add to dashboard Cite

With the development of social media, customers are sharing their experiences, and it is rapidly spreading as a form of online review. That is why the online review has become a significant information source affecting customers' purchase intention and behavior. Therefore, it is important to understand the customer's experience shown in the online review in order to maintain sustainable customer satisfaction and loyalty. The purpose of this study is to investigate what are the key attributes and the structural relationship of those key attributes. To accomplish this purpose, a total of 6596 hotel reviews were collected from Google (google.com). A frequency analysis using text mining was performed to figure out the most frequently mentioned attributes. In addition, semantic network analysis, factor analysis, and regression analysis were applied to understand the experience and satisfaction of the hotel customer. As a result, the top 99 keywords were divided into four groups such as "Intangible Service", "Physical Environment", "Purpose", and "Location". The factor analysis reduced the dimension of the original 64 keywords to 22 keywords, and grouped them into five factors, which are "Access", "F&B (Food and Beverage)", "Purpose", "Tangibles", and "Empathy". Based on these results, theoretical and practical implications for sustainable hotel marketing strategies are suggested.

show abstract

Section: Text Mining and Semantic Network Analysismentioning

confidence: 99%

Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

Ban

Choi

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…Semantic network analysis can identify unrevealed intrinsic meanings in context [70,78]. In addition, word frequency and cluster analysis can assist in understanding the influencing degree of a particular word and analyzing how a word affects the relationship between groups [78][79][80]. As a quantitative text analysis method, semantic network analysis provides an impressive theoretical and methodological foundation to describe the semantic nature of online reviews [80].…”

Section: Text Mining and Semantic Network Analysismentioning

confidence: 99%

An Exploratory Study of Electronic Word-of-Mouth Focused on Casino Hotels in Las Vegas and Macao

Tang

Kim

2022

Information

View full text Add to dashboard Cite

In order to investigate the key attributes of casino hotel customer eWOM and their structural relationships, this study selects two casino hotels located in Las Vegas and Macao. Through big data analytics, online reviews of two casino hotels from Google Travel were utilized. The frequency and CONCOR analyses showed the top 50 high-frequency words for each hotel and divided them into groups. The results of the factor analysis and linear regression analysis show that four factors, namely “Physical Environment”, “Entertainment”, “Experience”, and “Amenity”, in Las Vegas have a significant impact on customer satisfaction, while two factors, namely “Value” and “Physical Environment”, do in Macao. Through the results, the study points out the general characteristics affecting customer satisfaction of casino hotels, as well as the distinctions in influencing factors of their customer satisfaction in different source markets.

show abstract

“…Some of these representations try to reflect the context of user interaction with the learning scenario and the functionalities that are part of it. For example, Lim et al (2018) build a semantic representation to monitor student engagement and evaluate its impact on student performance in Massive Open Online Courses (MOOCs). Vlachostergiou et al ( 2016) used semantic technology to resolve the challenge of context-aware user modeling network communication in smart homes, where, like in an online laboratory, users interact with sensors and instruments.…”

Section: Semantic Representation Of Online Teaching Laboratorymentioning

confidence: 99%

A Study on the Effectiveness of an Undergraduate Online Teaching Laboratory With Semantic Mechanism From a Student Perspective

et al. 2020

View full text Add to dashboard Cite

Aim/Purpose: The current study was conducted to investigate the students’ perceived satisfaction with the use of a semantic-based online laboratory, which provides students with a search mechanism for laboratory resources, such as instruments and devices. Background: The increasing popularity of using online teaching labs, as an important element of experiential learning in STEM education, is because they represent a collection of integrated tools that allow students and teachers to interact and work collaboratively, whereas they provide an enriched learning content delivery mechanism. Moreover, several research studies have proposed various approaches for online teaching laboratories. However, there are hardly any studies that examine the student satisfaction provided by online laboratories based on students’ experiential learning. Methodology: To measure the effectiveness of the laboratory, we performed a case study in a Computer Fundamentals online course in which undergraduate students were able to manage devices and instruments remotely. Participants were a sample of 50 third semester students of Bachelor’s degree in Information Technology Administration who were divided in experimental and control groups (online laboratory vs. traditional manner). Given a laboratory assignment, students were able to carry out the management of devices and instruments through a LabView virtual environment and web services. The data of the experiment were collected through two questionnaires from both groups. The first is a system usability score (SUS) questionnaire concerning lab usability and the second one students’ cognitive load. Contribution: The results of the study showed a high correlation between usability and cognitive load-satisfaction of students who used the online teaching laboratory compared to the students who did not use it. Findings: On the one hand, the online laboratory provided students with an easy way to share and deploy instruments and devices, thus enhancing system usability. On the other hand, it offered important facilities which enabled students to customize the search for instruments and devices, which certainly had a positive impact on the relationship between cognitive load and satisfaction. Recommendations for Practitioners: In this work we propose an intuitive laboratory interface as well as easiness to use but challenging and capable of providing similar experiences to the traditional laboratory. Recommendation for Researchers: This study is one of the first to analyze the cognitive load-satisfaction relationship and compare it with usability scores. Impact on Society: Our analyses make an important contribution to the literature by suggesting a correlation analysis comparing the results of experimental and control groups that participated in this research work, in terms of usability and cognitive load-satisfaction. Future Research: Future work will also investigate other methodological aspects of instructional design with the aim to improve personalized learning and reinforce collaborative experiences, as well as to deal with problems related to laboratory access, such as authentication, scheduling, and interoperability.

show abstract

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

Cited by 20 publications

References 39 publications

Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

An Exploratory Study of Electronic Word-of-Mouth Focused on Casino Hotels in Las Vegas and Macao

A Study on the Effectiveness of an Undergraduate Online Teaching Laboratory With Semantic Mechanism From a Student Perspective

Contact Info

Product

Resources

About