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001398188 001__ 1398188
001398188 003__ OCoLC
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001398188 019__ $$a1076235146$$a1080598910$$a1086469511$$a1117883088
001398188 020__ $$a9789811306501$$q(electronic book)
001398188 020__ $$a9811306508$$q(electronic book)
001398188 020__ $$z9789811306495$$q(hardback)
001398188 020__ $$z9811306494$$q(hardback)
001398188 0247_ $$a10.1007/978-981-13-0650-1$$2doi
001398188 035__ $$aSP(OCoLC)1075587941
001398188 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dOCLCQ$$dEBLCP$$dYDXIT$$dOH1$$dUPM$$dMERER$$dOCLCF$$dOCLCQ$$dUKMGB$$dUKAHL$$dOCLCQ$$dUIU$$dOCLCQ$$dSFB$$dOCLCO$$dOCLCQ
001398188 049__ $$aISEA
001398188 050_4 $$aLB1027$$b.F76 2018
001398188 08204 $$a371.33/4$$223
001398188 24500 $$aFrontiers of cyberlearning :$$bemerging technologies for teaching and learning /$$cJ. Michael Spector [and eight others], editors.
001398188 264_1 $$aSingapore :$$bSpringer,$$c[2018]
001398188 300__ $$a1 online resource
001398188 336__ $$atext$$btxt$$2rdacontent
001398188 337__ $$acomputer$$bc$$2rdamedia
001398188 338__ $$aonline resource$$bcr$$2rdacarrier
001398188 347__ $$atext file$$bPDF$$2rda
001398188 4901_ $$aLecture notes in educational technology
001398188 504__ $$aIncludes bibliographical references.
001398188 5050_ $$aIntro; Contents; Learning Any Time, Anywhere: Big Educational Data from Smart Devices; 1 Introduction; 2 Mobile Practice of Course Content; 2.1 Smart Device Mobile Applications; 2.2 User Interface; 2.3 Algorithm Self-contained Within the Smart Device; 3 Data Messaging System; 3.1 Questions for Business Analytics; 3.2 Questions for Learning Science; 3.3 Data Fields and JSON Schema for the Messaging System; 3.4 JSON Schema; 3.5 Online and Offline Modes; 3.6 Receiving System; 4 Security and Privacy; 4.1 Data Encryption; 4.2 Access Policies and Controls; 4.3 Data Integrity; 4.4 Certifications.
001398188 5058_ $$a5 Data Processing and Analysis5.1 Apache Spark; 5.2 Processing Pipeline; 5.3 Filtering; 6 Managed Computing Environments and Cloud Computing; 6.1 Databricks; 7 Data Storage and Formatting; 7.1 Raw JSON in AWS S3 Cloud Storage; 7.2 Parquet Binary Files and Streaming to Improve Efficiency; 8 Learning Science and Analytics; 8.1 Learning Curves for Learning Objectives; 8.2 Confidence and Metacognition; 9 Data Visualization; 9.1 Data Exploration and Visualization Using Built-in Tools; 10 Relationship Between Research and Production; 10.1 Development, Test, and Production Environment.
001398188 5058_ $$a10.2 Software Development Life Cycle for Educational Apps11 Summary; References; Framing Learning Analytics and Educational Data Mining for Teaching: Critical Inferencing, Domain Knowledge, and Pedagogy; 1 Wired and Virtual Schools; 2 Learning Analytics and Educational Data Mining; 3 Implications for Teacher Training Validity and Inferencing; 4 Implications for Teacher Research-More Theory, Thicker Description; 5 Conclusion; References; Learning Traces, Competence Assessment, and Causal Inference for English Composition; 1 For Big Data in Education; 2 Competence; 3 Learning Traces.
001398188 5058_ $$a4 The Next Step: Causal Models5 The Case Study; 6 Big Data Architecture; 7 Conclusion; References; QUESGEN: A Framework for Automatic Question Generation Using Semantic Web and Lexical Databases; 1 Introduction; 2 Technology-Enhanced Question Generation Systems; 3 A Framework for Generating Adaptive Questions; 3.1 The Conceptual Design; 3.2 The Template-Based Question Generation Approach and Implementation; 4 Term Relevance Analysis; 4.1 Methodology; 4.2 Results; 4.3 Discussion; 5 Question Ranking Evaluation; 5.1 Methodology.
001398188 5058_ $$a5.2 Ranking Algorithm and Integration in the Question Generation Framework5.3 Evaluation; 5.4 Results and Discussion; 6 Conclusions; References; A Big Data Reference Architecture for Teaching Social Media Mining; 1 Introduction; 2 Foundation; 3 Solution Architecture; 4 Results; 4.1 Analysis of Twitter Sentiment Data of a U.S. Presidential Candidate; 4.2 Differences in the Usage of Twitter Between IOS and Android Device Users; 4.3 Analysis of Meetup RSVPs: How About Fake RSVPs; 5 Conclusion; References; Big Data in Education: Supporting Learners in Their Role as Reflective Practitioners.
001398188 506__ $$aAccess limited to authorized users.
001398188 520__ $$aThis book demonstrates teachers' and learners' experiences with big data in education; education and cloud computing; and new technologies for teacher support. It also discusses the advantages of using these frontier technologies in teaching and learning and predicts the future challenges. As such, it enables readers to better understand how technologies can improve learning and teaching experiences. It is intended for graduates and scholars in educational technology disciplines and anyone interested in the applications of frontier technologies in education.
001398188 588__ $$aOnline resource; title from digital title page (viewed on December 20, 2018).
001398188 650_0 $$aEducational innovations.
001398188 650_0 $$aComputer-assisted instruction.
001398188 650_0 $$aEducational technology.
001398188 650_6 $$aEnseignement$$xInnovations.
001398188 650_6 $$aEnseignement assisté par ordinateur.
001398188 650_6 $$aTechnologie éducative.
001398188 655_0 $$aElectronic books
001398188 7001_ $$aSpector, J. Michael,$$eeditor.
001398188 77608 $$iPrint version:$$tFrontiers of cyberlearning.$$dSingapore : Springer, [2018]$$z9811306494$$z9789811306495$$w(OCoLC)1032354154
001398188 830_0 $$aLecture notes in educational technology.
001398188 852__ $$bebk
001398188 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-13-0650-1$$zOnline Access$$91397441.1
001398188 909CO $$ooai:library.usi.edu:1398188$$pGLOBAL_SET
001398188 980__ $$aBIB
001398188 980__ $$aEBOOK
001398188 982__ $$aEbook
001398188 983__ $$aOnline
001398188 994__ $$a92$$bISE