A Comparative Study on Vectorization and Classification Techniques in Sentiment Analysis to Classify Student-Lecturer Comments

Rakhmanov, Ochilbek

doi:10.1016/j.procs.2020.11.021

Cited by 18 publications

(5 citation statements)

References 7 publications

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“…According to Leitner et al [36], higher education institutions collect and analyse data to gain insights and make predictions based on critical questions identified. The literature highlights various strategic objectives that are achieved through the use of learning analytics, such as administrative and resource planning [37], identifying at-risk students [4], [5], [38], understanding institutional successes and challenges [38], altering academic and pedagogical models [19], [39] identifying data and risk challenges [38], [40], analysing what-if scenarios and experimenting with different approaches [41], [42], increasing productivity and effectiveness [43], [44], measuring faculty activity [45], and helping learners understand their learning style [6]- [8], [46]. By leveraging learning analytics, higher education institutions can make data-driven decisions to improve their operations and enhance student success.…”

Section: A Learning Analyticsmentioning

confidence: 99%

“…To discover the interesting associative patterns, FP-Growth association rules mining algorithm was implemented using python programming language. In order to transform the dataset into a binomial structure that is compactible for the adopted FP-Growth Association rule mining algorithm each EI score was transformed into one of the following categories: poor (0-29), good (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44) or excellent (45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) depending on the EI score on the rescaled 60-point scale. This representation is consistent with the student academic performance grading system of both universities, with fails (0-49), passes and distinction (75-100).…”

Section: Association Rule Miningmentioning

confidence: 99%

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A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices

Oladipupo,

Samuel

2024

IEEE Access

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The connection between student-staff interaction, students' positive outcomes, and institutions has been widely studied as a key focus of research on student engagement and quality learning in higher education. In this study, a learning analytic approach is taken to establish a model for student-staff interaction. Two African institutions are engaged in the analysis for data acquisition. The two student engagement datasets used in this study are acquired by survey approach using National Survey of Student Engagement Instrument from the student perspectives. An association rule mining technique with Frequent Pattern Growth algorithm is implemented to discover interesting associative patterns among the student engagement indicators in relation to two student engagement datasets. Structural equation modelling was then employed to investigate the discovered interesting associative relationships. This study evaluated 16 different studentstaff interaction models using various fit indices to identify the most accurate predictor of student-staff interaction (SSI). The results suggest that poor quality interactions (QI), reflective and integrated learning (RI), and quantitative reasoning (QR) are key factors that influence the quality of SSI. The methodology and resulting validated models offer a unique contribution to the field and can inform the development of policies and best practices to enhance student engagement and improve learning outcomes in higher education.

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Section: A Learning Analyticsmentioning

confidence: 99%

Section: Association Rule Miningmentioning

confidence: 99%

A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices

Oladipupo,

Samuel

2024

IEEE Access

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“…Random Forest (RF) is considered a tree-based ensemble learning algorithm that uses multiple decision trees to provide an output [ 35 ]. The output is calculated using the random subset of features in each node of the decision tree.…”

Section: A Novel Student Modeling Approach: Student Classification Ratementioning

confidence: 99%

A novel color labeled student modeling approach using e-learning activities for data mining

Buyrukoğlu

2022

Univ Access Inf Soc

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Student modeling approaches are important to identify students’ needs, learning styles, and to monitor their improvements for individual modules. Lecturers may incorrectly identify the students’ needs and learning styles based on solely an exam grade or performance in the class. In doing so, students need to be classified using more parameters such as e-learning activities, attendance to virtual live class (for theory and practice) and submission time of the assignment, etc. This study proposes a novel color-labeled student modeling/classification approach using e-learning activities to identify students’ learning styles and to monitor students’ weekly improvements for individual modules. A novel Student Classification Rate (SCR) formula was created by combining three stages including pre-study stage, virtual_class stage, and virtual_LAB_class stage. In the evaluation part of the SCR, Artificial Neural Network and Random Forest algorithms were employed based on two different feature sets for an Object-Oriented Programming Module. Feature set 1 consisted of a combination of e-learning and regular data while the feature set 2 was referred as the combination of the SCR and the regular data. Random Forest yielded the lowest MAE (0.7) by using feature set 2. Also, the majority of the students’ (81%) learning styles referred to attending the live virtual class. Students’ weekly learning progress was also monitored successfully since the Pearson correlation was measured as 0.78 with the 95% confidence interval between the mean of SCR and lab grades. Additionally, SCR used for two more different modules yielded convincing results in the determination of students’ learning styles. The obtained results reveal that the proposed SCR approach has significant potential to correctly classify students, identify students’ learning styles, and help the lecturer to monitor the students' weekly progress. Finally, it seems that SCR would have a significant impact on improvement of students learning.

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“…To alleviate the discussed shortcomings, a hybrid approach (i.e., a combination of machine learning methods and lexicons) can help improve the sentiment classification performance. Since the text classification problem is a supervised learning task in which the class observations is predicted based on some feature values, a wide range of ML algorithms (e.g., Support Vector Machine (SVM) [12,1], Naive Bayes (NB) [12,38], decision tree [12], random forest [12,1], logistic regression, and neural networks [27,47,9,7]) can be incorporated.…”

Section: Supervised Text Classificationmentioning

confidence: 99%

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

Ghahramani¹,

Galle²,

Ratti³

et al. 2021

Preprint

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Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert assessments, including in-situ observations, surveys, and remote sensing analyses. Location data platforms, such as TripAdvisor, can provide people's opinion on many destinations and experiences, including UGS. This paper leverages Artificial Intelligence techniques for opinion mining and text classification using such platform's reviews as a novel approach to urban green space quality assessments. Natural Language Processing is used to analyze contextual information given supervised scores of words by implementing computational analysis. Such an application can support local authorities and stakeholders in their understanding of-and justification for-future investments in urban green space.

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A Comparative Study on Vectorization and Classification Techniques in Sentiment Analysis to Classify Student-Lecturer Comments

Cited by 18 publications

References 7 publications

A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices

A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices

A novel color labeled student modeling approach using e-learning activities for data mining

Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Space

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