2,019 research outputs found
Predicting student performance in interactive online question pools using mouse interaction features
Modeling student learning and further predicting the performance is a
well-established task in online learning and is crucial to personalized
education by recommending different learning resources to different students
based on their needs. Interactive online question pools (e.g., educational game
platforms), an important component of online education, have become
increasingly popular in recent years. However, most existing work on student
performance prediction targets at online learning platforms with a
well-structured curriculum, predefined question order and accurate knowledge
tags provided by domain experts. It remains unclear how to conduct student
performance prediction in interactive online question pools without such
well-organized question orders or knowledge tags by experts. In this paper, we
propose a novel approach to boost student performance prediction in interactive
online question pools by further considering student interaction features and
the similarity between questions. Specifically, we introduce new features
(e.g., think time, first attempt, and first drag-and-drop) based on student
mouse movement trajectories to delineate students' problem-solving details. In
addition, heterogeneous information network is applied to integrating students'
historical problem-solving information on similar questions, enhancing student
performance predictions on a new question. We evaluate the proposed approach on
the dataset from a real-world interactive question pool using four typical
machine learning models.Comment: 10 pages, 7 figures, conference lak20, has been accepted, proceeding
now. link: https://lak20.solaresearch.org/list-of-accepted-paper
Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network
Student performance prediction is critical to online education. It can
benefit many downstream tasks on online learning platforms, such as estimating
dropout rates, facilitating strategic intervention, and enabling adaptive
online learning. Interactive online question pools provide students with
interesting interactive questions to practice their knowledge in online
education. However, little research has been done on student performance
prediction in interactive online question pools. Existing work on student
performance prediction targets at online learning platforms with predefined
course curriculum and accurate knowledge labels like MOOC platforms, but they
are not able to fully model knowledge evolution of students in interactive
online question pools. In this paper, we propose a novel approach using Graph
Neural Networks (GNNs) to achieve better student performance prediction in
interactive online question pools. Specifically, we model the relationship
between students and questions using student interactions to construct the
student-interaction-question network and further present a new GNN model,
called R^2GCN, which intrinsically works for the heterogeneous networks, to
achieve generalizable student performance prediction in interactive online
question pools. We evaluate the effectiveness of our approach on a real-world
dataset consisting of 104,113 mouse trajectories generated in the
problem-solving process of over 4000 students on 1631 questions. The experiment
results show that our approach can achieve a much higher accuracy of student
performance prediction than both traditional machine learning approaches and
GNN models.Comment: 8 pages, 8 figures. Accepted at CIKM 202
Peer-inspired student performance prediction in interactive online question pools with graph neural network
Student performance prediction is critical to online education. It can
benefit many downstream tasks on online learning platforms, such as estimating
dropout rates, facilitating strategic intervention, and enabling adaptive
online learning. Interactive online question pools provide students with
interesting interactive questions to practice their knowledge in online
education. However, little research has been done on student performance
prediction in interactive online question pools. Existing work on student
performance prediction targets at online learning platforms with predefined
course curriculum and accurate knowledge labels like MOOC platforms, but they
are not able to fully model knowledge evolution of students in interactive
online question pools. In this paper, we propose a novel approach using Graph
Neural Networks (GNNs) to achieve better student performance prediction in
interactive online question pools. Specifically, we model the relationship
between students and questions using student interactions to construct the
student-interaction-question network and further present a new GNN model,
called R^2GCN, which intrinsically works for the heterogeneous networks, to
achieve generalizable student performance prediction in interactive online
question pools. We evaluate the effectiveness of our approach on a real-world
dataset consisting of 104,113 mouse trajectories generated in the
problem-solving process of over 4000 students on 1631 questions. The experiment
results show that our approach can achieve a much higher accuracy of student
performance prediction than both traditional machine learning approaches and
GNN models.Comment: 8 pages, 8 figures. Accepted at CIKM 202
QLens: Visual analytics of multi-step problem-solving behaviors for improving question design
With the rapid development of online education in recent years, there has
been an increasing number of learning platforms that provide students with
multi-step questions to cultivate their problem-solving skills. To guarantee
the high quality of such learning materials, question designers need to inspect
how students' problem-solving processes unfold step by step to infer whether
students' problem-solving logic matches their design intent. They also need to
compare the behaviors of different groups (e.g., students from different
grades) to distribute questions to students with the right level of knowledge.
The availability of fine-grained interaction data, such as mouse movement
trajectories from the online platforms, provides the opportunity to analyze
problem-solving behaviors. However, it is still challenging to interpret,
summarize, and compare the high dimensional problem-solving sequence data. In
this paper, we present a visual analytics system, QLens, to help question
designers inspect detailed problem-solving trajectories, compare different
student groups, distill insights for design improvements. In particular, QLens
models problem-solving behavior as a hybrid state transition graph and
visualizes it through a novel glyph-embedded Sankey diagram, which reflects
students' problem-solving logic, engagement, and encountered difficulties. We
conduct three case studies and three expert interviews to demonstrate the
usefulness of QLens on real-world datasets that consist of thousands of
problem-solving traces
An Investigation of the Correlation Between Mental Workload and Web User’s Interaction
Mental Workload, a psychological concept, was identified as being linked with task’s and system’s performance. In the context of Human-Computer Interaction, recent research has identified Mental Workload as an important measure in the designing and evaluation of web interfaces, and as an additional and supplemental insight to typical usability evaluation methods. Simultaneously, web logs containing data related to web users’ interaction (e.g. scrolling; mouse clicks) have been proved useful in evaluating the usability of web sites by analysing the data tracked for hundreds of users. In order to study if the potential of logs of user interaction can be applied in the study of Mental Workload in Web design, an online experiment with 145 participants was performed. Additionally, the experiment, composed of alternative interfaces, sought to assess the role of Mental Workload in the evaluation of interfaces using interactive Infographics, which were identified by literature as bringing new challenges and concerns in the field of Web Design. The online experiment’s results suggested that correlations between mental demands and users’ interaction can only be observed when taking in consideration the web interface used or the profile of the users. Moreover, the used measurement methods for assessing Mental Workload were not capable of predicting task performance, as previous research suggested (in the context of other types of web interfaces)
An investigation of the correlation between Mental Workload and Web User’s Interaction
Mental Workload, a Psychology concept, was identified as being linked with task’s and system’s performance. In the context of Human-Computer Interaction, recent research has identified Mental Workload as an important measure in the designing and evaluation of web interfaces, and as an additional and supplemental insight to typical Usability evaluation methods. Simultaneously, web logs containing data related to web users’ interaction (e.g. scrolling; mouse clicks) have been proved useful in evaluating the Usability of web sites by levering the data tracked for hundreds of users. In order to study if the potential of logs of user interaction can be applied in the study of Mental Workload in Web design, an online experiment with 145 participations was performed. Additionally, the experiment, composed of alternative interfaces, sought to assess the role of Mental Workload in the evaluation of interfaces using interactive Infographics, which were identified by literature as bringing new challenges and concerns in the field of Web Design. The online experiment’s results suggested that correlations between mental demands and users’ interaction can only be observed when taking in consideration the web interface used or the profile of the users. Moreover, the used measurement methods for assessing Mental Workload were not capable of predicting task performance, as previous research suggested (in the context of other types of web interfaces)
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
As an emerging education strategy, learnersourcing offers the potential for
personalized learning content creation, but also grapples with the challenge of
predicting student performance due to inherent noise in student-generated data.
While graph-based methods excel in capturing dense learner-question
interactions, they falter in cold start scenarios, characterized by limited
interactions, as seen when questions lack substantial learner responses. In
response, we introduce an innovative strategy that synergizes the potential of
integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM)
embeddings. Our methodology employs a signed bipartite graph to comprehensively
model student answers, complemented by a contrastive learning framework that
enhances noise resilience. Furthermore, LLM's contribution lies in generating
foundational question embeddings, proving especially advantageous in addressing
cold start scenarios characterized by limited graph data interactions.
Validation across five real-world datasets sourced from the PeerWise platform
underscores our approach's effectiveness. Our method outperforms baselines,
showcasing enhanced predictive accuracy and robustness
PREDIKSI TINGKAT PEMAHAMAN MATERI PESERTA DIDIK DALAM PEMBELAJARAN ONLINE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)
Penelitian ini bertujuan untuk memprediksi tingkat pemahaman materi peserta didik dalam pembelajaran online menggunakan algoritma Support Vector Machine (SVM). Algoritma ini digunakan agar mendapatkan penilaian terhadap pemahaman materi peserta didik yang lebih akurat dan objektif berdasarkan aktivitas peserta didik selama proses pembelajaran online. Penelitian ini menggunakan pendekatan eksperimen untuk mengimplementasikan algoritma Support Vector Machine (SVM), sehingga memiliki kinerja yang optimal dalam melakukan prediksi, menggunakan data dari Kaggle. Tahapan implementasi ini diantaranya data preprocessing yang terdiri dari Encoding Categorical dan normalisasi, kemudian proses pemodelan yaitu proses pelatihan dan pengujian model. Hasil implementasi algoritma Support Vector Machine (SVM) didapatkan akurasi sebesar 88.5% menggunakan parameter C = 24 dan gamma = 0.21, selain itu F1-Score yang didapatkan juga menunjukan hasil yang baik diantaranya kelas Low sebesar 88%, kelas Mid sebesar 87% dan kelas High sebesar 91%. Kemudian, fitur yang memiliki pengaruh sangat besar terhadap hasil prediksi tingkat pemahaman materi pembelajaran dalam pembelajaran online adalah fitur ketidakhadiran. This study aims to predict understanding level of students in online learning using the Support Vector Machine (SVM) algorithm. This algorithm is used in order to get an assessment understanding level of students that is more accurate and objective based on students activities during the online learning process. This study uses an experimental approach to implement the Support Vector Machine (SVM) algorithm, so that it has optimal performance in making predictions, using data from Kaggle. These stages of implementation include preprocessing data which consists of Encoding Categorical and Normalization, then the modeling process is the process of training and testing the model. The results of the implementation of the Support Vector Machine (SVM) algorithm obtained an accuracy of 88.5% using the parameters C = 24 and gamma = 0.21, besides that the F1-Score obtained also showed good results including the Low class 88%, the Mid class 87% and the High class 91%. Then, the feature that has a very big influence on the results of the prediction understanding level of students in online learning is the absence feature
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