Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Predicting Student Grade Based On Free Style Comments Using Word2vec And Ann By Considering Prediction Results Obtained In Consecutive Lessons
Download Predicting Student Grade Based On Free Style Comments Using Word2vec And Ann By Considering Prediction Results Obtained In Consecutive Lessons full books in PDF, epub, and Kindle. Read online Predicting Student Grade Based On Free Style Comments Using Word2vec And Ann By Considering Prediction Results Obtained In Consecutive Lessons ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Predicting Student Grade Based on Free-Style Comments Using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons by : Jingyi Luo
Download or read book Predicting Student Grade Based on Free-Style Comments Using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons written by Jingyi Luo and published by . This book was released on 2015 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuously tracking students during a whole semester plays a vital role to enable a teacher to grasp their learning situation, attitude and motivation. It also helps to give correct assessment and useful feedback to them. To this end, we ask students to write their comments just after each lesson, because student comments reflect their learning attitude towards the lesson, understanding of course contents, and difficulties of learning. In this paper, we propose a new method to predict final student grades. The method employs Word2Vec and Artifical Neural Network (ANN) to predict student grade in each lesson based on their comments freely written just after the lesson. In addition, we apply a window function to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. The experiment results show that the prediction correct rate reached 80% by considering the predicted student grades from six consecutive lessons, and a final rate became 94% from all 15 lessons. The results illustrate that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades as the lessons go by. [For complete proceedings, see ED560503.].
Book Synopsis Data Driven Approaches in Digital Education by : Élise Lavoué
Download or read book Data Driven Approaches in Digital Education written by Élise Lavoué and published by Springer. This book was released on 2017-09-04 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, held in Tallinn, Estonia, in September 2017. The 24 full papers, 23 short papers, 6 demo papers, and 22 poster papers presented in this volume were carefully reviewed and selected from 141 submissions. The theme for the 12th EC-TEL conference on Data Driven Approaches in Digital Education' aims to explore the multidisciplinary approaches thateectively illustrate how data-driven education combined with digital education systems can look like and what are the empirical evidences for the use of datadriven tools in educational practices.
Book Synopsis Advances in Knowledge Discovery and Data Mining by : James Bailey
Download or read book Advances in Knowledge Discovery and Data Mining written by James Bailey and published by Springer. This book was released on 2016-04-11 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016. The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.
Book Synopsis Neural Information Processing by : Derong Liu
Download or read book Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-11-07 with total page 951 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.
Book Synopsis Knowledge Science, Engineering and Management by : Cungeng Cao
Download or read book Knowledge Science, Engineering and Management written by Cungeng Cao and published by Springer Nature. This book was released on with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Social Informatics by : Steffen Staab
Download or read book Social Informatics written by Steffen Staab and published by Springer. This book was released on 2018-09-19 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 11185 + 11186 constitutes the proceedings of the 10th International Conference on Social Informatics, SocInfo 2018, held in Saint-Petersburg, Russia, in September 2018. The 30 full and 32 short papers presented in these proceedings were carefully reviewed and selected from 110 submissions. They deal with the applications of methods of the social sciences in the study of socio-technical systems, and computer science methods to analyze complex social processes, as well as those that make use of social concepts in the design of information systems.
Book Synopsis Predicting Student Performance Using Data from an Auto-grading System by : Huanyi Chen
Download or read book Predicting Student Performance Using Data from an Auto-grading System written by Huanyi Chen and published by . This book was released on 2018 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: As online auto-grading systems appear, information obtained from those systems can potentially enable the researchers to create predictive models to predict student behaviour and performances. In University of Waterloo, the ECE150 (Introductory Programming) Instructional Team wants insights into how to best allocate their limited teaching resources, especially individual tutoring, to achieve improved educational outcomes. However, currently, the Instructional Team allocates tutoring time in a reactive basis. They help students ''as-requested''. This approach serves those students with the wherewithal to request help, but many of the students who are struggling do not reach out for assistance. In ECE150 of year 2016, the Instructional Team had a hypothesis that the assignment grades may not be an accurate predictor of students' performance. Instead, they had another hypothesis that a behaviour analysis of student performance might be able to identify students for proactive intervention. Therefore, we, as the Research Team, want to explore what can be inferred from the students' behaviour, such as how frequently they submit, how early they submit for the first time, from the auto-grading data that can potentially allow us to identify students who need help. However, given the changing nature of the setup of auto-grading systems (for example, assignment content might be different from year to year), it is more important for us to explore the data and get insights, rather than trying to create a precise predictive model. 1. If we put students into categories according to their final exam and midterm performances, can we create a model over the auto-grading data to understand the students' behaviour and predict those categories? More importantly, to predict the students who need help and identify them as early as possible. 2. Can we predict students' raw numerical midterm grades and raw final exam grades from the students' behaviour? 3. Can we find any interesting relations between the features generated (reflecting students' behaviour) from auto-grading system information, grades and student categories? In our experiments, we generated different type of features based on the raw data we collected from the Marmoset of 428 first-year students in ECE150 of year 2016, such as the passing rate for each programming task, the testcase outcomes, the number of submissions, the lab attendance and the time interval of submissions. The experiments for those features are our first step for exploring the auto-grading data. However, we mentioned more features which are reasonable for conducting experiments in the thesis and future experiments will be conducted for them. We applied a decision-tree algorithm to all above features and a linear regression algorithm to the time intervals feature to predict the students' grades on their midterm and final exam. In all experiments, we split the data into training set and testing set. The training set was balanced by applying Synthetic Minority Oversampling Technique (SMOTE). For regression, we used the time interval between the student's first reasonable submission and the deadline as the feature, and applied linear regression algorithm to predict the exam grades. The results showed that for the midterm, the mean of difference between predicted midterm grades and actual midterm grades (maximum is 110 points) is -5.76 points and the standard deviation is 16.44 points. For the final exam, the mean of difference between predicted final exam grades and actual final exam grades (maximum is 120 points) is 0.92 points and the standard deviation is 17.12 points. In order to stabilize the residual variance, power transformation was applied. For classification, students were divided into three categories according to their midterm and final exam grades: good-performance students, satisfactory-performance students, and poor-performance students, and we used C4.5 decision tree algorithm to classify students. In order to take the regression model into comparison, we used the predicted midterm and final exam grades to create predicted categories for regression method. The results showed that for both midterm and final exam, the regression model using the time interval between the student's first reasonable submission and the deadline gave us the best Precision and F-measure for predicting which students would perform poorly on the exams. During the experiments, we found for predicting raw midterm grades or raw final exam grades, the time interval information from the assignment assigned right before the midterm exam or the final exam was most correlated with the midterm grades or final exam grades; however, if we considered midterm grades for the final exam, we found the correlation of the midterm grades was greater than the correlation of all assignments. The experiment results show that the linear regression model using submission time interval performs better than other models and further researching on this might be the best next step. However, since this is only a preliminary auto-grading data exploratory study, we can only get limited insight from the data and features. Future work will include performing additional experiments on combining different features to explore the data and as we collect more data, we can reach more definitive conclusions.
Book Synopsis Patterns, Predictions, and Actions: Foundations of Machine Learning by : Moritz Hardt
Download or read book Patterns, Predictions, and Actions: Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Book Synopsis Dive Into Deep Learning by : Joanne Quinn
Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
Book Synopsis Innovation in Information Systems and Technologies to Support Learning Research by : Mohammed Serrhini
Download or read book Innovation in Information Systems and Technologies to Support Learning Research written by Mohammed Serrhini and published by Springer Nature. This book was released on 2019-11-30 with total page 659 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides glimpses into contemporary research in information systems & technology, learning, artificial intelligence (AI), machine learning, and security and how it applies to the real world, but the ideas presented also span the domains of telehealth, computer vision, the role and use of mobile devices, brain–computer interfaces, virtual reality, language and image processing and big data analytics and applications. Great research arises from asking pertinent research questions. This book reveals some of the authors’ “beautiful questions” and how they develop the subsequent “what if” and “how” questions, offering readers food for thought and whetting their appetite for further research by the same authors.
Book Synopsis Linguistic Inquiry and Word Count by : James W. Pennebaker
Download or read book Linguistic Inquiry and Word Count written by James W. Pennebaker and published by Lawrence Erlbaum Assoc Incorporated. This book was released on 1999-04-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Language, whether spoken or written, is an important window into people's emotional and cognitive worlds. Text analysis of these narratives, focusing on specific words or classes of words, has been used in numerous research studies including studies of emotional, cognitive, structural, and process components of individuals' verbal and written language. It was in this research context that the LIWC program was developed. The program analyzes text files on a word-by-word basis, calculating percentage words that match each of several language dimensions. Its output is a text file that can be opened in any of a variety of applications, including word processors and spreadsheet programs. The program has 68 pre-set dimensions (output variables) including linguistic dimensions, word categories tapping psychological constructs, and personal concern categories, and can accommodate user-defined dimensions as well. Easy to install and use, this software offers researchers in social, personality, clinical, and applied psychology a valuable tool for quantifying the rich but often slippery data provided in the form of personal narratives. The software comes complete on one 31/2 diskette and runs on any Windows-based computer.
Book Synopsis Creating an Excellent School by : Hedley Beare
Download or read book Creating an Excellent School written by Hedley Beare and published by Routledge. This book was released on 2018-05-11 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally published in 1989. The pursuit of excellence is much discussed with reference to education, but the question remains, ’How can a school become excellent?’ This book demonstrates that excellence depends on good management which, in turn, depends not only on a clear understanding of good management theory, but on the ability to translate theory into practice. The authors offer profound insights into three crucial areas of leadership: culture, structure, and public accountability. Drawing on areas outside education, such as advertising and business, they discuss many innovations that are already current - flexitime, the vertical curriculum, mastery learning, community support - and depict ways in which these can be brought together into a total educational experience. More strikingly, however, they look ahead, examining the potential changes to our concept of schooling: for instance those brought about by the growth of information technology. This book emphasises that at the heart of outstanding schooling are visionary leadership, a clear sense of purpose, and creatively conceived and flexible support structures.
Book Synopsis Complex, Intelligent and Software Intensive Systems by : Leonard Barolli
Download or read book Complex, Intelligent and Software Intensive Systems written by Leonard Barolli and published by Springer Nature. This book was released on 2021-06-29 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the proceedings of the 15th International Conference on Complex, Intelligent, and Software Intensive Systems, which took place in Asan, Korea, on July 1–3, 2021. Software intensive systems are systems, which heavily interact with other systems, sensors, actuators, devices, and other software systems and users. More and more domains are involved with software intensive systems, e.g., automotive, telecommunication systems, embedded systems in general, industrial automation systems, and business applications. Moreover, the outcome of web services delivers a new platform for enabling software intensive systems. Complex systems research is focused on the overall understanding of systems rather than its components. Complex systems are very much characterized by the changing environments in which they act by their multiple internal and external interactions. They evolve and adapt through internal and external dynamic interactions. The development of intelligent systems and agents, which is each time more characterized by the use of ontologies and their logical foundations build a fruitful impulse for both software intensive systems and complex systems. Recent research in the field of intelligent systems, robotics, neuroscience, artificial intelligence, and cognitive sciences is very important factor for the future development and innovation of software intensive and complex systems. The aim of the book is to deliver a platform of scientific interaction between the three interwoven challenging areas of research and development of future ICT-enabled applications: Software intensive systems, complex systems, and intelligent systems.
Book Synopsis Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications by : Tran Khanh Dang
Download or read book Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications written by Tran Khanh Dang and published by Springer. This book was released on 2021-11-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 8th International Conference on Future Data and Security Engineering, FDSE 2021, held in Ho Chi Minh City, Vietnam, in November 2021.* The 28 full papers and 8 short were carefully reviewed and selected from 168 submissions. The selected papers are organized into the following topical headings: big data analytics and distributed systems; security and privacy engineering; industry 4.0 and smart city: data analytics and security; blockchain and access control; data analytics and healthcare systems; and short papers: security and data engineering. * The conference was held virtually due to the COVID-19 pandemic.
Book Synopsis Recommender Systems Handbook by : Francesco Ricci
Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Book Synopsis Text Analytics with Python by : Dipanjan Sarkar
Download or read book Text Analytics with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2019-05-21 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
Book Synopsis Artificial Intelligence in Medicine by : David Riaño
Download or read book Artificial Intelligence in Medicine written by David Riaño and published by Springer. This book was released on 2019-06-19 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
