Evaluating the impact of prior required scaffolding items on the improvement of student performance prediction

2020 ◽  
Vol 25 (4) ◽  
pp. 3227-3249
Author(s):  
Amal ASSELMAN ◽  
Mohamed KHALDI ◽  
Souhaib AAMMOU
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
The Impact

scholarly journals A New Approach to Modelling Students’ Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems

2021 ◽  
Vol 8 (3) ◽  
pp. 340-348
Author(s):  
Kouamé Abel ASSIELOU ◽  
Cissé Théodore HABA ◽  
Tanon Lambert KADJO ◽  
Bi Tra GOORE ◽  
Kouakou Daniel YAO
Keyword(s):  
Student Performance ◽  
Performance Prediction ◽  
Intelligent Tutoring Systems ◽  
Cognitive Abilities ◽  
Mean Squared Error ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Social And Emotional ◽  
Computer Based Learning ◽  
The Impact

Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called SoEmo-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.

Transitioning to Blended Learning: Understanding Student and Faculty Perceptions

2011 ◽  
Vol 15 (1) ◽  
Author(s):  
Nanette P. Napier ◽  
Sonal Dekhane ◽  
Stella Smith
Keyword(s):  
Blended Learning ◽  
Student Performance ◽  
Liberal Arts ◽  
Faculty Perceptions ◽  
Liberal Arts College ◽  
Final Exam ◽  
Face To Face ◽  
Faculty Teaching ◽  
Face To Face Instruction ◽  
The Impact

This paper describes the conversion of an introductory computing course to the blended learning model at a small, public liberal arts college. Blended learning significantly reduces face-to-face instruction by incorporating rich, online learning experiences. To assess the impact of blended learning on students, survey data was collected at the midpoint and end of semester, and student performance on the final exam was compared in traditional and blended learning sections. To capture faculty perspectives on teaching blended learning courses, written reflections and discussions from faculty teaching blended learning sections were analyzed. Results indicate that student performance in the traditional and blended learning sections of the course were comparable and that students reported high levels of interaction with their instructor. Faculty teaching the course share insights on transitioning to the blended learning format.

The Impact of PowerPoint on Student Performance and Course Evaluations in Economics Courses: An Experiment at Six Institutions

2008 ◽  
Author(s):  
Lisa Daniels ◽  
John C. Kane ◽  
Brian P. Rosario ◽  
Thomas A. Creahan ◽  
Carlos F. Liard-Muriente ◽  
...  
Keyword(s):  
Student Performance ◽  
Course Evaluations ◽  
Economics Courses ◽  
The Impact

An Exploratory Study of the Online Learning Environment

2013 ◽  
Vol 29 (1) ◽  
pp. 117-147 ◽  
Author(s):  
Cynthia J. Khanlarian ◽  
Rahul Singh
Keyword(s):  
Learning Environment ◽  
Student Performance ◽  
It Use ◽  
Environment Impact ◽  
Web Based ◽  
Improve Student Performance ◽  
Research Questions ◽  
The Impact ◽  
Relevant Factors ◽  
Significant Factors

ABSTRACT Web-based homework (WBH) is an increasingly important phenomenon. There is little research about its character, the nature of its impact on student performance, and how that impact evolves over an academic term. The primary research questions addressed in this study are: What relevant factors in a WBH learning environment impact students' performance? And how does the impact of these factors change over the course of an academic term? This paper examines and identifies significant factors in a WBH learning environment and how they impact student performance. We studied over 300 students using WBH extensively for their coursework, throughout a semester in an undergraduate class at a large public university. In this paper, we present factors in the WBH learning environment that were found to have a significant impact on student performance during the course of a semester. In addition to individual and technological factors, this study presents findings that demonstrate that frustration with IT use is a component of the learning environment, and as a construct, has a larger impact than usefulness on student performance at the end of a course. Our results indicate that educators may benefit from training students and engaging them in utility of co-operative learning assignments to mitigate the level of frustration with the software in the WBH learning environment and improve student performance.

scholarly journals The Influence of Video Format on Engagement and Performance in Online Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 128
Author(s):  
Sergej Lackmann ◽  
Pierre-Majorique Léger ◽  
Patrick Charland ◽  
Caroline Aubé ◽  
Jean Talbot
Keyword(s):  
Student Performance ◽  
Cognitive Engagement ◽  
Emotional Engagement ◽  
Emotional States ◽  
Lecture Capture ◽  
Improved Performance ◽  
And Performance ◽  
Subject Experiment ◽  
The Impact ◽  
Video Format

Millions of students follow online classes which are delivered in video format. Several studies examine the impact of these video formats on engagement and learning using explicit measures and outline the need to also investigate the implicit cognitive and emotional states of online learners. Our study compared two video formats in terms of engagement (over time) and learning in a between-subject experiment. Engagement was operationalized using explicit and implicit neurophysiological measures. Twenty-six (26) subjects participated in the study and were randomly assigned to one of two conditions based on the video shown: infographic video or lecture capture. The infographic video showed animated graphics, images, and text. The lecture capture showed a professor, providing a lecture, filmed in a classroom setting. Results suggest that lecture capture triggers greater emotional engagement over a shorter period, whereas the infographic video maintains higher emotional and cognitive engagement over longer periods of time. Regarding student learning, the infographic video contributes to significantly improved performance in matters of difficult questions. Additionally, our results suggest a significant relationship between engagement and student performance. In general, the higher the engagement, the better the student performance, although, in the case of cognitive engagement, the link is quadratic (inverted U shaped).

scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

The impact of learning environment disruption on medical student performance

2013 ◽  
Vol 47 (2) ◽  
pp. 210-213 ◽  
Author(s):  
Tim J Wilkinson ◽  
Anthony N Ali ◽  
Caroline J Bell ◽  
Frances A Carter ◽  
Chris M Frampton ◽  
...  
Keyword(s):  
Medical Student ◽  
Learning Environment ◽  
Student Performance ◽  
The Impact

scholarly journals Small Changes: Using Assessment to Direct Instructional Practices in Large-Enrollment Biochemistry Courses

2017 ◽  
Vol 16 (1) ◽  
pp. ar7 ◽  
Author(s):  
Xiaoying Xu ◽  
Jennifer E. Lewis ◽  
Jennifer Loertscher ◽  
Vicky Minderhout ◽  
Heather L. Tienson
Keyword(s):  
Hydrogen Bonding ◽  
Student Performance ◽  
Bond Energy ◽  
Multiple Choice ◽  
Instructional Interventions ◽  
Student Responses ◽  
Large Classes ◽  
Instructional Effect ◽  
Large Enrollment ◽  
The Impact

Multiple-choice assessments provide a straightforward way for instructors of large classes to collect data related to student understanding of key concepts at the beginning and end of a course. By tracking student performance over time, instructors receive formative feedback about their teaching and can assess the impact of instructional changes. The evidence of instructional effectiveness can in turn inform future instruction, and vice versa. In this study, we analyzed student responses on an optimized pretest and posttest administered during four different quarters in a large-enrollment biochemistry course. Student performance and the effect of instructional interventions related to three fundamental concepts—hydrogen bonding, bond energy, and pKa—were analyzed. After instructional interventions, a larger proportion of students demonstrated knowledge of these concepts compared with data collected before instructional interventions. Student responses trended from inconsistent to consistent and from incorrect to correct. The instructional effect was particularly remarkable for the later three quarters related to hydrogen bonding and bond energy. This study supports the use of multiple-choice instruments to assess the effectiveness of instructional interventions, especially in large classes, by providing instructors with quick and reliable feedback on student knowledge of each specific fundamental concept.

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