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ردیف | عنوان | نوع |
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1 |
Data-driven detection and characterization of communities of accounts collaborating in MOOCs
شناسایی و توصیف مبتنی بر داده جوامع حسابهایی که در MOOC همکاری میکنند-2021 Collaboration is considered as one of the main drivers of learning and it has been broadly studied
across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs
has risen exponentially during the last years and there have been a number of works focused
on studying collaboration. However, these previous studies have been restricted to the analysis
of collaboration based on the forum and social interactions, without taking into account other
possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work
we performed a case study with the goal of implementing a data-driven approach to detect and
characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based
on their submission times to quizzes as an indicator of collaboration, and applied it to data from
two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in
couples and larger communities exhibiting different types of associations between user accounts. The
characterization of these user accounts suggested that some of them might represent genuine online
learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or
multiple account cheating might be present. These findings call for additional research on the study
of the kind of collaborations that can emerge in online settings.
keywords: تجزیه و تحلیل یادگیری | داده کاوی آموزشی | یادگیری مشارکتی | دوره های آنلاین گسترده باز | هوش مصنوعی | Learning analytics | Educational data mining | Collaborative learning | Massive open online courses | Artificial intelligence |
مقاله انگلیسی |
2 |
A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision
راهنمای حاشیه نویسی ویدئوی حین عمل جراحی مغز و اعصاب برای تجزیه و تحلیل یادگیری ماشین و بینایی ماشین-2021 - OBJECTIVE: Computer vision (CV) is a subset of artificial
intelligence that performs computations on image or video
data, permitting the quantitative analysis of visual information. Common CV tasks that may be relevant to surgeons
include image classification, object detection and tracking,
and extraction of higher order features. Despite the potential
applications of CV to intraoperative video, however, few
surgeons describe the use of CV. A primary roadblock in
implementing CV is the lack of a clear workflow to create an
intraoperative video dataset to which CV can be applied. We
report general principles for creating usable surgical video
datasets and the result of their applications.
- METHODS: Video annotations from cadaveric endoscopic endonasal skull base simulations (n [ 20 trials of 1e5 minutes, size [ 8 GB) were reviewed by 2 researcher annotators. An internal, retrospective analysis of workflow for development of the intraoperative video annotations was performed to identify guiding practices. - RESULTS: Approximately 34,000 frames of surgical video were annotated. Key considerations in developing annotation workflows include 1) overcoming software and personnel constraints; 2) ensuring adequate storage and access infrastructure; 3) optimization and standardization of annotation protocol; and 4) operationalizing annotated data. Potential tools for use include CVAT (Computer Vision Annotation Tool) and Vott: open-sourced annotation software allowing for local video storage, easy setup, and the use of interpolation. - CONCLUSIONS: CV techniques can be applied to surgical video, but challenges for novice users may limit adoption. We outline principles in annotation workflow that can mitigate initial challenges groups may have when converting raw video into useable, annotated datasets. Key words: Artificial intelligence | Computer vision | Intraoperative video | Machine learning |
مقاله انگلیسی |
3 |
Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images
تشخیص خطای هوشمند رادیاتور خنک کننده بر اساس تجزیه و تحلیل یادگیری عمیق از تصاویر حرارتی مادون قرمز-2019 Detection of faults and intelligent monitoring of equipment operations are essential for modern industries.
Cooling radiator condition is one of the factors that affects engine performance. This paper proposes a novel and
accurate radiator condition monitoring and intelligent fault detection based on thermal images and using a deep
convolutional neural network (CNN) which has a specific configuration to combine the feature extraction and
classification steps. The CNN model is constructed from VGG-16 structure that is followed by batch normalization
layer, dropout layer, and dense layer. The suggested CNN model directly uses infrared thermal images as
input to classify six conditions of the radiator: normal, tubes blockage, coolant leakage, cap failure, loose
connections between fins & tubes and fins blockage. Evaluation of the model demonstrates that leads to results
better than traditional computational intelligence methods, such as an artificial neural network, and can be
employed with high performance and accuracy for fault diagnosis and condition monitoring of the cooling
radiator under various working circumstances. Keywords: Cooling radiator | Fault detection | Thermal image analysis | Deep learning | Convolutional neural network |
مقاله انگلیسی |
4 |
Educational data mining and learning analytics for 21st century higher education: A review and synthesis
داده کاوی آموزشی و تجزیه و تحلیل یادگیری برای آموزش عالی قرن بیست و یکم: بررسی و ترکیب-2019 The potential influence of data mining analytics on the students’ learning processes and outcomes
has been realized in higher education. Hence, a comprehensive review of educational data
mining (EDM) and learning analytics (LA) in higher education was conducted. This review
covered the most relevant studies related to four main dimensions: computer-supported learning
analytics (CSLA), computer-supported predictive analytics (CSPA), computer-supported behavioral
analytics (CSBA), and computer-supported visualization analytics (CSVA) from 2000 till
2017. The relevant EDM and LA techniques were identified and compared across these dimensions.
Based on the results of 402 studies, it was found that specific EDM and LA techniques could
offer the best means of solving certain learning problems. Applying EDM and LA in higher
education can be useful in developing a student-focused strategy and providing the required tools
that institutions will be able to use for the purposes of continuous improvement. Keywords: Data analytics | Educational data mining | Learning analytics | Higher education |
مقاله انگلیسی |
5 |
Utilizing early engagement and machine learning to predict student outcomes
استفاده از تعامل اولیه و یادگیری ماشین برای پیش بینی نتایج دانش آموزان-2019 Finding a solution to the problem of student retention is an often-required task across Higher
Education. Most often managers and academics alike rely on intuition and experience to identify the
potential risk students and factors. This paper examines the literature surrounding current methods and
measures in use in Learning Analytics. We find that while tools are available, they do not focus on
earliest possible identification of struggling students. Our work defines a new descriptive statistic for
student attendance and applies modern machine learning tools and techniques to create a predictive
model. We demonstrate how students can be identified as early as week 3 (of the Fall semester) with
approximately 97% accuracy. We, furthermore, situate this result within an appropriate pedagogical
context to support its use as part of a more comprehensive student support mechanism. Keywords: Machine learning | Learning analytics | Student retentionMSC: 68-U35 68-T10 97-B40 |
مقاله انگلیسی |
6 |
Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis
پیش بینی بهبود عصبی خوب بعد از ایست قلبی خارج از بیمارستان: تجزیه و تحلیل یادگیری ماشین-2019 Background: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in
OHCA patients.
Methods: Adult OHCA patients who had a presumed cardiac etiology and a sustained return of spontaneous circulation between 2013 and 2016 were
analyzed; 80% of the individuals were analyzed for training and 20% were analyzed for validation. We developed using six machine learning algorithms:
logistic regression (LR), extreme gradient boosting (XGB), support vector machine, random forest, elastic net (EN), and neural network. Variables that
could be obtained within 24 hours of the emergency department visit were used. The area under the receiver operation curve (AUROC) was calculated to
assess the discrimination. Calibration was assessed by the Hosmer–Lemeshow test. Reclassification was assessed by using the continuous net
reclassification index (NRI).
Results: A total of 19,860 OHCA patients were included in the analysis. Of the 15,888 patients in the training group, 2228 (14.0%) had a good
neurological recovery; of the 3972 patients in the validation group, 577 (14.5%) had a good neurological recovery. The LR, XGB, and EN models showed
the highest discrimination powers (AUROC (95% CI)) of 0.949 (0.941–0.957) for all), and all three models were well calibrated (Hosmer–Lemeshow test:
p >0.05). The XGB model reclassified patients according to their true risk better than the LR model (NRI: 0.110), but the EN model reclassified patients
worse than the LR model (NRI:
1.239).
Conclusion: The best performing machine learning algorithm was the XGB and LR algorithm . Keywords: Out-of-hospital cardiac arrest | Outcome | Machine learning analysis |
مقاله انگلیسی |
7 |
Utilizing early engagement and machine learning to predict student outcomes
استفاده از تعامل اولیه و یادگیری ماشین برای پیش بینی نتایج دانش آموزان-2019 Finding a solution to the problem of student retention is an often-required task across Higher
Education. Most often managers and academics alike rely on intuition and experience to identify the
potential risk students and factors. This paper examines the literature surrounding current methods and
measures in use in Learning Analytics. We find that while tools are available, they do not focus on
earliest possible identification of struggling students. Our work defines a new descriptive statistic for
student attendance and applies modern machine learning tools and techniques to create a predictive
model. We demonstrate how students can be identified as early as week 3 (of the Fall semester) with
approximately 97% accuracy. We, furthermore, situate this result within an appropriate pedagogical
context to support its use as part of a more comprehensive student support mechanism. Keywords: Machine learning | Learning analytics | Student retentionMSC: 68-U35 | 68-T10 | 97-B40 |
مقاله انگلیسی |
8 |
Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections
پل تجزیه و تحلیل یادگیری و پردازش شناختی برای طبقه بندی داده های بزرگ در مجموعه های ویدئویی میکرو یادگیری-2018 Moving towards the next generation of personalized learning environments requires intelligent ap
proaches powered by analytics for advanced learning contexts with enriched digital content. Micro
Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm
for delivering short educational videos in small pre-organized chunks over time, so that learners can get
knowledge in a manageable way. However, with the ever-increasing number of videos, it has become
challenging to arrange and search them according to specific categories. In this paper, we get around the
problem by bridging Learning Analytics and Cognitive Computing to analyze the content of large video
collections, going over traditional term-based methods. We propose an efficient and effective approach
to automatically classify a collection of educational videos on pre-existing categories which uses (i) a
Speech-to-Text tool to get video transcripts, (ii) Natural Language Processing and Cognitive Computing
methods to extract semantic concepts and keywords from video transcripts for their representation, and
(iii) Apache Spark as Big Data technology for scalability. Several classifiers are trained on the feature
vectors extracted by Cognitive Computing tools. Then, we compared our approach with other combi
nations of state-of-the-art feature types and classifiers over a large-scale dataset we collected from
Coursera. Considering the experimental results, we expect our approach can facilitate the development
of Learning Analytics tools powered by Cognitive Computing to support content managers on micro
learning video management while improving how learners search videos.
Keywords: Cognitive Computing ، Big Data technologies ، Micro-learning video ، Multi-class classification ، Learning Analytics ، Video classification |
مقاله انگلیسی |
9 |
نقش شخصیت در یادگیری مبتنی بر کامپیوتر
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 22 امروزه با توجه به پیشرفت های بسیار سریع تکنولوژی خصوصا در زمینه ابزارهای آموزشی،فناوری های جدیدی پدیدار شده اندکه یکی از آنها یادگیری مبتنی بر کامپیوتر می باشد.علی رغم فواید کاملا قابل شهود این نوع یادگیری افرادی وجود دارند که یادگیری سنتی را ترجیح داده و بر این اعتقاد هستند که بازخورد مثبتی از یادگیری مبتنی بر کامپیوتر نداشته اند.با توجه به این که افراد دارای شخصیت ها و واکنش های مختلفی در رفتار با سیستم می باشند بنابراین چالش اصلی در این زمینه شناسایی انواع شخصیت های موجود می باشد تا بتوان تفاوت های یادگیرندگان را سنجش و بررسی نمود تا برنامه ای مطابق انواع رفتار افراد درگیر در این سیستم طراحی شود.شخصیت به عنوان یکی از مهمترین پارامترهای تفاوت های فردی میان اشخاص مطرح می شود.در این مقاله بررسی می شود که چگونه تفاوت های شخصیتی در یادگیرندگان بر یادگیری مبتنی بر کامپیوتر تاثیر گذار است و هر کدام ازاین شخصیت ها چه واکنشی نشان خواهند داد.نتایج قابل توجه از نوزده پژوهش صورت گرفته در این زمینه در زیر ذکر شده است:
- MBTIبیشترین تاکید را بر شخصیت در یادگیری مبتنی بر کامپیوتر دارد. -ویژگی های شخصیتی بر اینکه چگونه یادگیرندگان مفاهیم آموزشی و شیوه یادگیری مانند جمع آوری اطلاعات و برقراری ارتباط بامدرس و شیوه مطالعه را ترجیح می دهند تاثیر میگذارند. -یک مدل جدید از متغیر های شخصیت در یادگیری مبتنی بر کامپیوتر با تلاش و نظرسنجی بسیاری از پژوهشگران علاقمند و پزشکان توانای مرتبط با این حوزه باید در نظر گرفته شود. - رویکرد ضمنی جدیدی را با استفاده از تجزیه و تحلیل یادگیری به جای شیوه پرسشنامه برای شناسایی شخصیت یادگیرنده مطرح می کند. واژه های کلیدی: یادگیری مبتنی بر کامپیوتر | مدل شخصیت | سیستم آموزش هوشمند | یادگیری تطبیقی |
مقاله ترجمه شده |
10 |
Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners Engagement Rate
مراحل اولیه تعامل: بکارگیری تحلیل داده های بزرگ در محیط یادگیری مشارکتی برای اندازه گیری نرخ تعامل آموزندگان-2016 Computer-supported Collaborative Learning
(CSCL) is a pedagogical strategy associated with how learners
construct knowledge with a group by computer-based learning
system. In recent years, most of the computer-based learning
systems record the interaction log of each learner when
developing course assignments. However, the recorded data
is facing a challenge to expose the learners behaviors during
the course and to design a computer-supported collaborative
learning activity. To address those challenges in this paper, a
novel collaborative programming tool called Software Project
Development and Insight Learning Environment (SPDI Learning
Environment) is described. The SPDI Learning Environment
allows learners of computer science to develop course assignments
collaboratively. Besides, it allows instructors to investigate the
learners behaviors by associating a web-based integrated
development environment (IDE) with Big Data analysis pipeline
and Visualization Dashboard. In addition to collect real data
from courses, we designed learning activities to help teachers to
engage the field of CSCL and Learning Analytics.
Keywords: Computer | supported Collaborative Learning |Learning Analytics | Collaborative Programming | Clickstream Data | Big-Data Analysis |
مقاله انگلیسی |