scholarly journals A Data Mining Method for Students' Behavior Understanding

2020 ◽  
Vol 15 (06) ◽  
pp. 18
Author(s):  
Wei Na
Keyword(s):  
Student Behavior ◽  
Behavioral Model ◽  
Recall Rate ◽  
Learning Performance ◽  
Multiple Perspectives ◽  
Additional Information ◽  
Behavior Understanding ◽  
Learning Path ◽  
High Recall Rate ◽  
Collaborative Filtering Algorithm

To model students' behavior and describe their behavior characteristics accurately and comprehensively, a framework for predicting students' learning performance based on behavioral model is proposed, which extracts features from multiple perspectives to describe behaviors more comprehensively, including statistical features and association features. In addition, a multi-task model is designed for fine-grained prediction of students' learning performance in the curriculum. A framework for predicting mastery based on online learning behavior is also put forward. Additional context information is added to the collaborative filtering algorithm, including student-knowledge-point mastery and class-knowledge-point, and students' mastery is predicted according to the learning path excavated. Considering the time-varying of mastery, the approximate curve of students' mastery of knowledge points is fitted according to the Ebinhaus forgetting curve. The experiments show that the proposed framework has a high recall rate for the prediction of learning performance, and also shows a certain practicability for early warning. Further, based on the model, the correlation between student behavior patterns and learning performance is discussed. The addition of additional information has improved the prediction efficiency, especially the operational efficiency. At the same time, the proposed framework can not only dynamically assess students' master of knowledge, but also facilitate the system to review feedback or adjust the learning order, and provide personalized learning services.

scholarly journals Curriculum Assessment of Higher Educational Institution Using Trace-segmented Clustering

2018 ◽  
Vol 20 (1) ◽  
pp. 33-48
Author(s):  
Satrio Adi Priyambada ◽  
Mahendrawathi ER ◽  
Bernardo Nugroho Yahya
Keyword(s):  
Educational Institution ◽  
Research Area ◽  
Learning Performance ◽  
Learning Behavior ◽  
Sequence Matching ◽  
Curriculum Assessment ◽  
Learning Path ◽  
And Performance ◽  
Curriculum Guideline ◽  
The Impact

Curriculum mining is research area that assess students’ learning behavior and compare it with the curriculum guideline. Previous work developed sequence matching alignment approach to check the conformance between students’ learning behavior and curriculum guideline. Considering only the sequence matching alignment is insufficient to understand the patterns of group of students. Another work proposed an approach by aggregating the students’ profile to represent students’ learning behavior and investigate the impact of the learning behavior to their learning performance. However, the aggregate profile approach considers the entire period of study rather than segmented period. This study proposes a methodology to assess students’ learning path with segmented period i.e. the semester of the related curriculum. The segmented-period profile generated would be the input for sequence matching alignment approach to assess the conformity of students’ behavior with the prior curriculum guideline. Real curriculum data has been used to test the effectivity of the methodology. The results show that the students can be grouped into various cluster per semesters that have different characteristic with respect to their learning behavior and performance. The results can be analyzed further to improve the curriculum guideline.

scholarly journals Exploring high reliable substructures in auto-reconstructions of a neuron

2021 ◽  
Author(s):  
Yishan He ◽  
Jiajin Huang ◽  
Gaowei Wu ◽  
Jian Yang
Keyword(s):  
State Of The Art ◽  
Recall Rate ◽  
Local Alignment ◽  
Alignment Algorithm ◽  
Neuron Tracing ◽  
Digital Reconstruction ◽  
High Recall Rate ◽  
Multiple Species ◽  
Multiple Reference ◽  
Precision Rate

Abstract The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron’s reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.

Library book intelligent recommendation system based on artificial intelligence

2021 ◽  
pp. 1-6
Author(s):  
Lu Pang
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Absolute Error ◽  
Recall Rate ◽  
Data Set ◽  
Evaluation Data ◽  
Artificial Intelligence Technology ◽  
Collaborative Filtering Algorithm ◽  
Book Evaluation ◽  
Evaluation Matrix

In order to improve the accuracy of intelligent recommendation of library books, an intelligent recommendation system of library books based on artificial intelligence was designed. The system uses artificial intelligence technology to clean up and normalize the data, automatically extracts the user’s historical evaluation data of books, divides the whole user space into several similar user clusters through the similar user clustering module, constructs the user book evaluation matrix according to the historical evaluation data, and uses the hybrid collaborative filtering algorithm which integrates user based and project-based to predict each user a book evaluation matrix of similar user clusters was used to realize the intelligent recommendation of library books, and the recommendation results were displayed to users through the user interface module. The results show that the average absolute error and root mean square error of the system are always the lowest, and the recommendation accuracy is high. When the control parameter is 0.4, the best intelligent book recommendation effect can be obtained; the recommended recall rate is not affected by the sparse density of the data set, and the stability is strong.

scholarly journals SOFIA: A Novel Automated Breast Ultrasound System Used on Patients in the Prone Position: A Pilot Study on Lesion Detection in Comparison to Handheld Grayscale Ultrasound

2018 ◽  
Vol 78 (05) ◽  
pp. 499-505 ◽  
Author(s):  
André Farrokh ◽  
Harika Erdönmez ◽  
Fritz Schäfer ◽  
Nicolai Maass
Keyword(s):  
Prone Position ◽  
Recall Rate ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
The Novel ◽  
Scanning Time ◽  
High Recall Rate ◽  
Study Population ◽  
Sensitivity Specificity ◽  
Automated Breast Ultrasound

Abstract Introduction Most of the currently available automated breast ultrasound systems require patients to be in the supine position. Previous data, however, show a high recall rate with this method due to artifacts. The novel automated breast ultrasound scanner SOFIA scans the breast with the patient in a prone position, resulting in even compression of breast tissue. We present our initial results with this examination method. Material and Methods 63 patients were analyzed using a handheld B-mode ultrasound. In cases of BI-RADS 1, 2 or 5, a SOFIA scan was performed. Sensitivity, specificity and accuracy were calculated. Interobserver agreement was evaluated using Cohenʼs kappa. The duration of the scan was measured for both methods. Results No BI-RADS 5 lesion was missed with SOFIA. The SOFIA had an additional recall rate of 16.67% compared to B-mode ultrasound. The sensitivity, specificity and accuracy of SOFIA was 100, 83.33 and 88.89%, respectively. Cohenʼs kappa showed substantial agreement (κ = 0.769) between examiner 1 (B-mode) and examiner 2 (SOFIA). The mean scan duration for the B-mode system and the SOFIA system was 24.21 minutes and 12.94 minutes, respectively. In four cases, D-cup breasts were not scanned in their entirety. Conclusion No cancer was missed when SOFIA was used in this preselected study population. The scanning time was approximately half of that required for B-mode ultrasound. The additional unnecessary recall rate was 16.67%. Larger D cup-size breasts were difficult to position and resulted in an incomplete image in four cases.

scholarly journals IterCluster: a barcode clustering algorithm for long fragment read analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8431
Author(s):  
Jiancong Weng ◽  
Tian Chen ◽  
Yinlong Xie ◽  
Xun Xu ◽  
Gengyun Zhang ◽  
...  
Keyword(s):  
De Novo Assembly ◽  
Clustering Algorithm ◽  
De Novo ◽  
Recall Rate ◽  
Divide And Conquer ◽  
Data Set ◽  
Target Region ◽  
Additional Information ◽  
A Genome ◽  
Long Fragment Read

Recent advances in long fragment read (LFR, also known as linked-read technologies or read-cloud) technologies, such as single tube long fragment reads (stLFR), 10X Genomics Chromium reads, and TruSeq synthetic long-reads, have enabled efficient haplotyping and genome assembly. However, in the case of stLFR and 10X Genomics Chromium reads, the long fragments of a genome are covered sparsely by reads in each barcode and most barcodes are contained in multiple long fragments from different regions, which results in inefficient assembly when using long-range information. Thus, methods to address these shortcomings are vital for capitalizing on the additional information obtained using these technologies. We therefore designed IterCluster, a novel, alignment-free clustering algorithm that can cluster barcodes from the same target region of a genome, using -mer frequency-based features and a Markov Cluster (MCL) approach to identify enough reads in a target region of a genome to ensure sufficient target genome sequence depth. The IterCluster method was validated using BGI stLFR and 10X Genomics chromium reads datasets. IterCluster had a higher precision and recall rate on BGI stLFR data compared to 10X Genomics Chromium read data. In addition, we demonstrated how IterCluster improves the de novo assembly results when using a divide-and-conquer strategy on a human genome data set (scaffold/contig N50 = 13.2 kbp/7.1 kbp vs. 17.1 kbp/11.9 kbp before and after IterCluster, respectively). IterCluster provides a new way for determining LFR barcode enrichment and a novel approach for de novo assembly using LFR data. IterCluster is OpenSource and available on https://github.com/JianCong-WENG/IterCluster.

A learning resource recommendation algorithm based on online learning sequential behavior

2019 ◽  
Vol 17 (02) ◽  
pp. 1940001 ◽  
Author(s):  
Xuebin Wang ◽  
Zhengzhou Zhu ◽  
Jiaqi Yu ◽  
Ruofei Zhu ◽  
DeQi Li ◽  
...  
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Recall Rate ◽  
Learning Resources ◽  
Recommendation Algorithm ◽  
Sequential Behavior ◽  
Learning Resource ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student’s online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student’s learning events from his/her online learning process. Then each student’s learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student’s learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student’s feature vector. Moreover, we improve the H-[Formula: see text] clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm.

scholarly journals A Shallow–Deep Feature Fusion Method for Pedestrian Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9202
Author(s):  
Daxue Liu ◽  
Kai Zang ◽  
Jifeng Shen
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Recall Rate ◽  
Fusion Method ◽  
Discriminative Power ◽  
Deep Feature ◽  
Proposal A ◽  
High Recall Rate ◽  
Feature Based

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

scholarly journals A New Learning Path Model for E-Learning Systems

2021 ◽  
Vol 19 (2) ◽  
pp. 20-40
Author(s):  
David Brito Ramos ◽  
Ilmara Monteverde Martins Ramos ◽  
Isabela Gasparini ◽  
Elaine Harada Teixeira de Oliveira
Keyword(s):  
Student Behavior ◽  
Group Formation ◽  
Learning Systems ◽  
Learning System ◽  
Path Model ◽  
New Approach ◽  
Learning Path ◽  
E Learning ◽  
Learning Paths ◽  
Collaborative Activities

This work presents a new approach to the learning path model in e-learning systems. The model uses data from the database records from an e-learning system and uses graphs as representation. In this work, the authors show how the model can be used to represent visually the learning paths, behavior analysis, help to suggest group formation for collaborative activities, and thus assist the teacher in making decisions. To validate the practical utility of the model, the authors created two tools, one to visualize the learning paths and another to suggest groups of students for collaborative activities. Both tools were tested in a real environment, presenting useful results. The authors carried experiments with students from three programs: physics, electrical engineering, and computer science. Experiments show that it is possible to use the proposed learning path to analyze student behavior patterns and recommend group formation with positive results.

scholarly journals Effect of Using Twitter in Teaching Parasitology in King Abduaziz University, Jeddah, Saudi Arabia

2017 ◽  
Vol 10 (2) ◽  
pp. 50
Author(s):  
Najia A. Al-Zanbagi
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Learning Community ◽  
Student Behavior ◽  
Action Theory ◽  
Communicative Action ◽  
Learning And Teaching ◽  
Behavior Understanding ◽  
Content Sharing ◽  
Education Experience

The evolution of Social Web, particularly Social Media makes users interaction with Internet massive ideal proceeding. Web technologies have completely improved the Internet dynamics and allowing users to originate texts, images or video as well as to share and participate through huge geographical limits. This research trial explores the Saudi girls Parasitology student’ behavior, understanding and effectiveness toward using Twitter in supporting learning and teaching aims. A main target was to raise discussion among students and promote learning via supporting student time on goal. Our innovative attempt followed guidelines lay in the Learning and Teaching such as Communicative Action Theory to increase the student education experience through strong connections and enlarge content sharing between girl students, for the sake of building social collaborative learning community. By using this method, we found different girl student comprehension of using Twitter, some have very positive views to be as a tool for supporting lectures while some views consider twitter have small interest to the students own learning.Keywords: Twitter, Parasitology, Saudi Arabia, Social Media, Communicative Action theory,

scholarly journals PurExt: Automated Extraction of the Purpose-Aware Rule from the Natural Language Privacy Policy in IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lu Yang ◽  
Xingshu Chen ◽  
Yonggang Luo ◽  
Xiao Lan ◽  
Li Chen
Keyword(s):  
Data Collection ◽  
Natural Language ◽  
Recall Rate ◽  
Semantic Role Labeling ◽  
Privacy Policies ◽  
Novel Approach ◽  
High Recall Rate ◽  
Iot Devices ◽  
Machine Readable ◽  
The Internet Of Things

The extensive data collection performed by the Internet of Things (IoT) devices can put users at risk of data leakage. Consequently, IoT vendors are legally obliged to provide privacy policies to declare the scope and purpose of the data collection. However, complex and lengthy privacy policies are unfriendly to users, and the lack of a machine-readable format makes it difficult to check policy compliance automatically. To solve these problems, we first put forward a purpose-aware rule to formalize the purpose-driven data collection or use statement. Then, a novel approach to identify the rule from natural language privacy policies is proposed. To address the issue of diversity of purpose expression, we present the concepts of explicit and implicit purpose, which enable using the syntactic and semantic analyses to extract purposes in different sentences. Finally, the domain adaption method is applied to the semantic role labeling (SRL) model to improve the efficiency of purpose extraction. The experiments that are conducted on the manually annotated dataset demonstrate that this approach can extract purpose-aware rules from the privacy policies with a high recall rate of 91%. The implicit purpose extraction of the adapted model significantly improves the F1-score by 11%.

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