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Intelligent Tutoring Systems

Overview of attention for book
Cover of 'Intelligent Tutoring Systems'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Learning Early-Warning Model Based on Knowledge Points
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    Chapter 2 Adaptive Learning Spaces with Context-Awareness
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    Chapter 3 An Adaptive Approach to Provide Feedback for Students in Programming Problem Solving
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    Chapter 4 Analysis and Prediction of Student Emotions While Doing Programming Exercises
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    Chapter 5 Analyzing the Group Formation Process in Intelligent Tutoring Systems
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    Chapter 6 Analyzing the Usage of the Classical ITS Software Architecture and Refining It
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    Chapter 7 Assessing Students’ Clinical Reasoning Using Gaze and EEG Features
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    Chapter 8 Computer-Aided Intervention for Reading Comprehension Disabilities
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    Chapter 9 Conceptualization of IMS that Estimates Learners’ Mental States from Learners’ Physiological Information Using Deep Neural Network Algorithm
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    Chapter 10 Data-Driven Student Clusters Based on Online Learning Behavior in a Flipped Classroom with an Intelligent Tutoring System
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    Chapter 11 Decision Support for an Adversarial Game Environment Using Automatic Hint Generation
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    Chapter 12 Detecting Collaborative Learning Through Emotions: An Investigation Using Facial Expression Recognition
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    Chapter 13 Fact Checking Misinformation Using Recommendations from Emotional Pedagogical Agents
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    Chapter 14 Intelligent On-line Exam Management and Evaluation System
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    Chapter 15 Learning by Arguing in Argument-Based Machine Learning Framework
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    Chapter 16 Model for Data Analysis Process and Its Relationship to the Hypothesis-Driven and Data-Driven Research Approaches
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    Chapter 17 On the Discovery of Educational Patterns using Biclustering
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    Chapter 18 Parent-Child Interaction in Children’s Learning How to Use a New Application
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    Chapter 19 PKULAE: A Learning Attitude Evaluation Method Based on Learning Behavior
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    Chapter 20 Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities
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    Chapter 21 Predicting Subjective Enjoyment of Aspects of a Videogame from Psychophysiological Measures of Arousal and Valence
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    Chapter 22 Providing the Option to Skip Feedback – A Reproducibility Study
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    Chapter 23 Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing
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    Chapter 24 Representing and Evaluating Strategies for Solving Parsons Puzzles
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    Chapter 25 Testing the Robustness of Inquiry Practices Once Scaffolding Is Removed
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    Chapter 26 Toward Real-Time System Adaptation Using Excitement Detection from Eye Tracking
  28. Altmetric Badge
    Chapter 27 Towards Predicting Attention and Workload During Math Problem Solving
Attention for Chapter 20: Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Citations

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Readers on

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Summary X Dimensions citations
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Chapter title
Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week’s Activities
Chapter number 20
Book title
Intelligent Tutoring Systems
Published in
arXiv, June 2019
DOI 10.1007/978-3-030-22244-4_20
Book ISBNs
978-3-03-022243-7, 978-3-03-022244-4
Authors

Ahmed Alamri, Mohammad Alshehri, Alexandra Cristea, Filipe D. Pereira, Elaine Oliveira, Lei Shi, Craig Stewart, Alexandra I. Cristea, Alamri, Ahmed, Alshehri, Mohammad, Cristea, Alexandra, Pereira, Filipe D., Oliveira, Elaine, Shi, Lei, Stewart, Craig

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 12%
Student > Postgraduate 5 10%
Student > Master 5 10%
Student > Bachelor 4 8%
Professor 3 6%
Other 10 19%
Unknown 19 37%
Readers by discipline Count As %
Computer Science 11 21%
Social Sciences 7 13%
Engineering 3 6%
Mathematics 2 4%
Arts and Humanities 2 4%
Other 3 6%
Unknown 24 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 August 2020.
All research outputs
#18,684,243
of 23,150,406 outputs
Outputs from arXiv
#542,157
of 952,359 outputs
Outputs of similar age
#262,241
of 351,135 outputs
Outputs of similar age from arXiv
#15,851
of 28,055 outputs
Altmetric has tracked 23,150,406 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 952,359 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,135 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28,055 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.

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