Topic-based knowledge mining of online student reviews for strategic planning in universities

Srinivas, Sharan; Rajendran, Suchithra

doi:10.1016/j.cie.2018.06.034

Cited by 61 publications

(45 citation statements)

References 24 publications

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“…There were two studies that used data from MOOC available from American universities: [74] used data concerning a total of 1,117,411 students of three datasets obtained from the Massachusetts Institute of Technology (MIT), Harvard; and in [84], the data considered regard 29,604 students enrolled in eleven public online classes from Stanford University. Finally, the study in [114] was performed with data obtained from six unidentified universities, and the study described in [115] used data obtained on 16,066 students enrolled on a public American university.…”

Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

“…We also found that some of the analysed studies had very specific objectives. These include implementing retention strategies [75] (retention here means to convince students to not desert or drop out); identifying student satisfaction [114]; recommending strategies to reduce attrition by reducing dropout [76]; analysing students' learning behaviour through the creation of a feature matrix for keeping information related to the local correlation of learning behaviour [72]; analysing activity, polarity, and emotions of students and tutors to perform sentiment analysis to help in dropout prediction [73]; monitoring the learning process and performing student profiling to support pedagogical actions to reduce dropout [80]; predicting remedial actions [87]; using data to improve courses [96] and learning experiences [96]; and exploring relationships between programming behaviour, student participation, and the outcomes obtained [112].…”

Section: How Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

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How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

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Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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Section: Where Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

Section: How Has La Been Deployed In the Studies Produced?mentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

View full text Add to dashboard Cite

show abstract

“…The importance of analyzing customer reviews (OCRs) has risen dramatically with the expansion of social media websites, and has allowed consumers to assess the quality of a product or service through other people's opinions ( Srinivas and Rajendran, 2019 ). With the increasing accessibility of the Internet, online customer interactions and postings are viewed by thousands of potential purchasers every day, so the distribution of positive reviews is crucial.…”

Section: Introductionmentioning

confidence: 99%

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services

Rajendran

Pagel

2020

Heliyon

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The effects of traffic congestion are adverse, primarily including air pollution, commuter stress, an increase in vehicle operating costs, and accidents on road. In efforts to alleviate these problems in metropolitan cities, logistics companies plan to introduce a new Urban Air Mobility (UAM) service called air taxis. These are electric-powered vehicles that would be tested and operated in the forthcoming years by international transportation companies like Airbus, Uber, and Kitty Hawk. Since these flying taxis are an emerging mode of transportation, it is necessary to provide recommendations for initial design, implementation, and operation. This study proposes managerial insights for these upcoming UAM services by analyzing online customer reviews and conducting an internal assessment of helicopter operations. Helicopters are similar to air taxis in regards to their vertical takeoff and landing (VTOL) operations, and therefore, customer reviews pertaining to the former can enable us to obtain insights into the strengths and weaknesses of the short-distance aviation service, in general. A four-stage sequential approach is used in this research, wherein online reviews are mined in Stage 1 , analyzed using bigram and trigram models in Stage 2 , 7S internal assessment is conducted for helicopter services in Stage 3 , and managerial recommendations for air taxis are proposed in Stage 4 . The insights obtained in this paper could assist any air taxi company in providing better customer service when they venture into the market.

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“…We choose VADER because it is attuned to sentiments expressed in social media texts (Hutto and Gilbert, 2016), and its consistent performance across varying datasets (Ribeiro et al, 2016). Considered as one of the best unsupervised sentiment analysis methods (Ribeiro et al, 2016;Soleymani et al, 2017), VADER has been adopted in studies to extract sentiment from eWOM such as Amazon reviews of video games (Zhang et al, 2019), reviews of restaurants (Deng et al, 2019) and online student reviews (Srinivas and Rajendran, 2019). Another reason for using VADER is its ability to detect sentiment in movie reviews.…”

Section: Investigating the Effects Of Textual Reviewsmentioning

confidence: 99%

Investigating the effects of textual reviews from consumers and critics on movie sales

Deng

2020

OIR

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PurposeThe purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).Design/methodology/approachThe paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.FindingsCritic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.Practical implicationsThis study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.Originality/valueThe major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

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Topic-based knowledge mining of online student reviews for strategic planning in universities

Cited by 61 publications

References 24 publications

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services

Investigating the effects of textual reviews from consumers and critics on movie sales

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