Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

sebis research profile

2,239 views

Published on

Research profile
Lehrstuhl für Informatik 19 - sebis
Fakultät für Informatik der TU München

  • Be the first to comment

  • Be the first to like this

sebis research profile

  1. 1. sebis Research Profile 20.7.2014, Prof. Dr. Florian Matthes Software Engineering für betriebliche Informationssysteme (sebis) Fakultät für Informatik Technische Universität München wwwmatthes.in.tum.de
  2. 2. Research background Enterprise Architecture Management Communities Collaborative Work Digital Content Social Software Engineering § System cartography § EAM tool surveys § EAM pattern catalog § Capability models in mergers & acquisitions § Building blocks for EAM § Wiki4EAM § Agile EAM § User-centered social software § Authorization models in social software § Introspective model-driven development § Enterprise 2.0 tool surveys § Hybrid Wikis § Tag-based knowledge organization Technology Transfer Projects § CoreMedia AG (Spinoff) § infoAsset AG (Spinoff) § Business & IT transformation @ VW § EAM 2.0 @ HUK Coburg § KPI systems @ SFS § Cloud security @ Siemens § Strategy assessment @ FI § D-MOVE more > Sebis Research Profile © sebis 2
  3. 3. Team Social Software Engineering more > Alexander Schneider Matheus Hauder Klym Shumaiev Thomas Reschenhofer Marin Zec Florian Matthes Bernhard Waltl Aline Schmidt Jian Kong Enterprise Architecture Management Alexander Waldmann Sebis Research Profile © sebis 3
  4. 4. Project partners since 2002 Enterprises and public administrations Deutsche Börse Systems Sebis Research Profile © sebis 4
  5. 5. Project partners since 2002 Consultants and software vendors Sebis Research Profile © sebis 5
  6. 6. Academic education Bachelor Informatics § Introduction to Software Engineering § Software Engineering for Business Applications § Software Engineering in Industry and Practice Master Informatics § Strategic IT Management and EAM § Web Application Engineering § Software Architectures § Global Software Engineering § GFSU (Startups, Entrepreneurship) Life-Long Learning § Euro CIO Professional Programme in Business and Enterprise Architecture § EAMKON Conference Series § Softwareforen Leipzig Working Group EAM more > Sebis Research Profile © sebis 6
  7. 7. Prototypical Solutions Informatics Engineering Evaluation Application Domain Practical Experience Research approach Information & Communication Technology Informatics Models Application Abstraction Spin-Off Sebis Research Profile © sebis 7
  8. 8. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 8
  9. 9. The adoption rate for new technologies keeps accelerating. Forbes Magazine July 7th 1997 Sebis Research Profile © sebis 9
  10. 10. Exponential growth starts inconspicuously, and humans are not used to reasoning about non-linear processes. Google Trends December 2013 Sebis Research Profile © sebis 10
  11. 11. An enterprises understood as an adaptive system of systems Humans: Employees, Customers, Suppliers, Partners, Markets, Communities, … Laws & Regulations Enterprise Business Capabilities Vision, Goals, Strategy OPTIMIZE TRANSFORM Information Management IM Capabilities Goals, Strategy OPTIMIZE TRANSFORM Resources: Energy, Matter, Information, Technology… Sebis Research Profile © sebis 11
  12. 12. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 12
  13. 13. Motivation – Most frequent EA challenges 100,00% 90,00% 80,00% 70,00% 60,00% 50,00% 40,00% 30,00% 20,00% 10,00% 0,00% 1. Ad hoc EAM demands 2. Unclear business goals 3. Hard to find experienced enterprise architects 4. EA demands unclear for EAM team 5. Enterprise environment changes too quickly Agree (%) Neither (%) Disagree (%) n=102 Hauder, M., Roth, S., Schulz, C., Matthes, F.: Organizational Factors Influencing Enterprise Architecture Management Challenges, 21st European Conference on Information Systems (ECIS 2013), Utrecht, Netherland, 2013. 13 Sebis Research Profile © sebis
  14. 14. Agile EA management principles Individuals and interactions over formal processes and tools Project managers EA Team Software architects Software developers IT Project 1 IT Project 2 IT Project 3 Top management Business stakeholders Software development IT operations Top management Strategy office Business owners Application owners IT operations Purchasing • Ensure top management support • Maintain a good relationship to people form other management areas Sebis Research Profile © sebis 14
  15. 15. Agile EA management principles Focus on demands of top stakeholders and speak their languages Œ  model collect motivate Architecture blueprints Business and IT strategy Business and org. constraints Individual architecture aspects Project managers communicate explain involve support EA Team get feedback Architecture-approval and requirements Architecture changes Software architects Stakeholder-specific architecture views Metrics Visualizations Reports Software developers IT Project 1 IT Project 2 IT Project 3 Top management Business stakeholders Software development IT operations Top management Strategy office Business owners Application owners IT operations Purchasing • A single number or picture is more helpful than 1000 reports • Communicate, communicate, communicate • Avoid waste • Benefit form existing model management processes Sebis Research Profile © sebis 15
  16. 16. Agile EA management principles Reflect behavior and adapt to changes Œ  model collect motivate adapt EA Team get feedback reflect Ž Architecture blueprints Business and IT strategy Business and org. constraints Individual architecture aspects Project managers communicate explain involve support Architecture-approval and requirements Architecture changes Software architects Stakeholder-specific architecture views Metrics Visualizations Reports Software developers IT Project 1 IT Project 2 IT Project 3 Top management Business stakeholders Software development IT operations Top management Strategy office Business owners Application owners IT operations Purchasing • Iterative and Incremental (one cycle ~12 months) • Use building blocks and Sebis Research Profile © sebis 16 patterns • Request 360° feedback • Adapt models and processes • Continuous collaboration
  17. 17. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 17
  18. 18. Using quantitative models in the context of EAM System behavior (dynamic) con strains change change con strains System structure (EA, static) 풕−ퟏ t = 푵푶푾 con strains 풕+ퟏ 1. Assess the architecture with metrics 2. Measure architecture changes 3. Plan architecture changes 4. Monitor system performance with KPIs (Business & IT) Sebis Research Profile © sebis 18
  19. 19. Metric Management Method (MMM) as Extension of the BEAMS Conceptual Framework Stakeholders Goals + Concerns Organizational Organizational Organizational context context Context Implementation Guide (Patterns & Building Blocks) EA Metric VBB Performance Indicator VBB VBB IBB EA Metric IBB IBB + EAM Metric Catalog Enterprise Architects Enterprise Architects Actors Development method Characterize situation Configure EAM function Analyze EAM function Adapt and evolve EAM function Execute EAM function BEAMS , EAM Pattern Catalog and EAM KPI Catalog Sebis Research Profile © sebis 19
  20. 20. Integrated software support for quantitative models in the domain of EAM Best practices for EAM metrics & performance measurement § KPI template § KPI catalog § Method for designing a KPI system Integrated Software Support § Query language for KPI definition over complex information models § KPI visualization (in progress) Evaluation § Siemens Financial Services § Credit Suisse, Bayern LB, Commerzbank, CALM3 Sebis Research Profile © sebis 20
  21. 21. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 21
  22. 22. What are current problems in EA model maintenance? N=125, 2013 Challenge n % of all Huge data collection effort 77 55.00% Low EA model data quality 77 55.00% Insufficient tool support 48 34.29% No management support 44 33.43% Low return on investment 36 25.71% Other 32 22.86% No specific challenge 10 7.14% Type of collection n % of all Manually from applications/databases 95 76.00% Manually via interviews 85 68.00% Manually modeled in workshops 66 52.80% Manually via questionnaires 46 36.80% Partially collected automatically 44 35.20% More > Sebis Research Profile © sebis 22
  23. 23. Federated enterprise architecture model management Modeling communities, artifacts, processes and their interactions Enterprise E EAM Metamodel and Model D Task fit Technology Team Metamodel Mappings Instance Mappings Modeling Community Modeling Experts PPM Metamodel and Model A Task fit Technology model and meta-model changes to be integrated Team publish model changes Federated EA Model Management • Importing • Differencing • Conflict detection • Conflict resolution • Collaboration • Negotiation BPM Metamodel and Model B Task fit Technology Team publish model changes ITSM Metamodel and Model C Task fit publish model changes Technology Team publish model changes Sebis Research Profile © sebis 23
  24. 24. Federated enterprise architecture model management Tool support - ModelGlue 1. Import of different models in a metamodel-based EA tool 2. Synchronization via model merging Provide means to identify model elements within the originating information source 3. Conflict detection during merge operation § Instance conflicts § Schema conflicts § Schema/instance conflicts 4. Collaborative conflict resolution Fine-grained access control is employed to find the organizational role in a chain of responsibility 5. Customizable conflict resolution strategy For further information see https://wwwmatthes.in.tum.de/pages/kkdtsjtjkc2g Sebis Research Profile © sebis 24
  25. 25. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 25
  26. 26. CALM3: Complexity of application landscapes Models, metrics and methods Research questions § What does "IT-complexity“ mean? § How can complexity be described? § Which factors drive application landscape complexity? § How can complexity be quantified? § How can complexity models contribute to landscape planning? Project Partners 10 Industry experts CALM3 Workshop Series Quarterly meetings Extensive EA data Concrete metrics Visionary discussions Tool development Sebis Research Profile © sebis 26
  27. 27. The complexity cube Sebis Research Profile © sebis 27
  28. 28. The complexity cube Classifying EA literature EA Complexity Publications ACN D1 ACN D2 ACN D3 ACN D4 Janssen et al. (2006) qualitative structural, dynamic objective ordered Buckl et al. (2009) qualitative structural objective ordered Saat et al. (2009) qualitative structural, dynamic objective ordered Dern et al. (2009) quantitative structural objective disordered Mocker (2009) quantitative structural objective disordered Zadeh et al. (2012) qualitative, quantitative structural objective ordered Kandjani et al. (2012) quantitative structural objective ordered Kandjani et al. (2013) qualitative, quantitative dynamic objective ordered Schütz et al. (2013) quantitative structural objective disordered Lagerström et al. (2013) quantitative structural objective disordered Trend: qualitative à quantitative Underrepresented: dynamic, subjective Sebis Research Profile © sebis 28
  29. 29. Classification of applications Visualizing the Hidden Structure of Application Landscapes § Calculation base: AL topology (applications, information flows) § Calculation: transitive dependencies of each application Classification § Largest cyclic group à Core § More outgoing dependencies à Control § More incoming dependencies à Shared § Less incoming dependencies à Periphery Propagation cost § Part of the AL affected by change § Sum of dependencies / applications2 2 1 3 4 5 8 9 Control Core Shared Periphery Lagerstrom, Robert, Carliss Y. Baldwin, Alan MacCormack, and Stephan Aier. "Visualizing and Measuring Enterprise Application Architecture: An Exploratory Telecom Case." Harvard Business School Working Paper, No. 13-103, June 2013. 7 6 Sebis Research Profile © sebis 29
  30. 30. EA complexity metric based on heterogeneity Complexity of Enterprise Architectures § Elements (amount & heterogeneity) § Relationships (amount & heterogeneity) Calculation of heterogeneity § Shannon entropy § No effect of proportional changes § Significant impact of small changes Example § Heterogeneity of database systems 1 0,8 0,6 0,4 0,2 0 Oracle DB2 SQL Server MySQL EM = 0.7 EMA = 2 N = 4 Schütz, A.; Widjaja, T.; Kaiser, J. (2013). Complexity in Enterprise Architectures - Conceptualization and Introduction of a Measure from a System Theoretic Perspective. European Conference on Information Systems (ECIS); Utrecht, Netherlands. Sebis Research Profile © sebis 30
  31. 31. Data collection § 6 companies (Financial services and Automotive) § More than 20 metrics found Metrics on Application level § Number of Business Functions (3/6) § Number of Infrastructure Components (4/6) Metrics on Domain level § Number of Applications (4/6) § Number of Information Flows (6/6) § Standard conformity (4/6) § Number of Function Points (3/6) § Functional redundancy (6/6) Application Domain Reoccurring AL complexity metrics in practice Application Application Sebis Research Profile © sebis 31
  32. 32. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 32
  33. 33. Semantic processing of legal texts for IT compliance 1. Interpreting legal texts is non-trivial § > 6000 laws and regulations in Germany § Words and expression are hard to understand § Uncertain, abstract, indeterminate legal terms § adequate, effective, appropriate etc. § International agreements and regulations 2. Compliance is desirable but expensive 3. Information systems can support compliance during the § creation, § exploration, § search, § interpretation and § visualization processes. Basel II / III Sarbanes- Oxley Act REACH Sebis Research Profile © sebis 33
  34. 34. Semantic processing of legal texts for IT compliance Company Employees Assets Tasks Objectives Requirements Engineering IT Requirements (Business IT Alignment) IT Systems COBIT TOGAF Controlling Support through IS Compliance Requirements (Legal Obligations) searching, exploration, interpretation, change tracking etc. § Information-systems LexInform, Juris, RIS, … Laws KWG, TMG, BDSG, … Authorities (e.g. BaFin) Sebis Research Profile © sebis 34
  35. 35. Semantic processing of legal texts for IT compliance Compliance Requirements Controlling (Legal Obligations) searching, exploration, interpretation, change tracking etc. § §44 IT-examination, auditing, (internal/external) revision, etc. Information-systems LexInform, Juris, RIS, … Laws/ Regulations KWG, TMG, BDSG, … Authorities (e.g. BaFin) 1. Information Retrieval (IR) § Searching, finding and exploring of information in unstructured documents § Meet the demand of information 2. Artificial Intelligence (AI) § Automatically derive new information / knowledge § Answer questions: § How has process XY be implemented in order to be compliant? à NO automation but decision-support Sebis Research Profile © sebis 35
  36. 36. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 36
  37. 37. Collaborative knowledge work is ubiquitous in organizations Solving complex problems in Development of large software systems communities Producing new ideas and innovations How can software support processes for collaborative knowledge work? Sebis Research Profile © sebis 37
  38. 38. Theoretical basis of the research project involves three different disciplines Knowledge Work Literature on knowledge work in organizations provides an understanding of the problem. Description of the problem: • Characteristics of knowledge work • Complex vs. Complicated problems • Roles in knowledge work Adaptive Case Management Adaptive case management is a novel approach to support knowledge-intensive processes. Solution ideas from ACM: • Essential requirements for ACM support • Emergent design of processes • Evolution of processes with templates Social Principles and Patterns Knowledge work relies on the successful collaboration of different roles. Facilitating collaboration: • Building successful online communities • Learning from existing communities on the web • Principles and patterns Sebis Research Profile © sebis 38
  39. 39. Solution: Empowering users to collaboratively structure knowledge-intensive processes Goal Orientation • Describe which goals should be achieved • Goals guide the stream of work • Replaces traditional process model Emergence • Empowerment and participation of end users • Adaptability of templates at run-time • Continuous improvement of templates Data Centricity • Data as driver for knowledge work • Goal-oriented transformation of data • Integration of processes and data Collaboration • Knowledge creation through interaction • Building a successful online community Case Templates • Sharing and preservation of knowledge • Access to recurring best practice patterns Logical and temporal dependencies with CMMN Create a new task for „Neue Idee“ Adding a new task Drag and drop of attributes on tasks Attribute types Hide completed tasks Access rights on attributes Completed tasks Unstructured information In-place editing New attribute for the template 2. LITERATURE REVIEW 4. CASE STUDIES 5. EVALUATION Sebis Research Profile © sebis 39 Design Principles § Flexible stage-gate process for Innovation Management § Development of a future Enterprise Architecture state § Artefact-oriented Requirements Engineering processes with templates Case Studies Analysis of related work and identification of research questions for three domains. ! ! ! Evaluation 1 Evaluation 2 Evaluation 3 Prototype for collaborative structuring of knowledge-intensive processes. 1. RESSCOPE EARCH Derivation of requirements for an Adaptive Case Management solution. 3. PROTOTYPE Case studies to support processes for all three investigated domains. Qualitative evaluation of the three case studies with expert interviews. Deliverable: Transcript of expert interviews Deliverable: Implemented prototype Deliverable: Research questions Deliverable: Requirements for Adaptive Case Management Deliverable: Prototype applied in three sample domains ? ? ? EA Management Innovation Management Requirements Engineering
  40. 40. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 40
  41. 41. Spreadsheets 2.0 Motivation Business users love spreadsheets § Declarative and interactive paradigm to capture functional dependencies § Modeling, analysis, simulation, visualization § Empowerment of business-users § Emergent structures (data, logic) Limitations of spreadsheets § Collaborative work § Complex linked data social networks, logistic networks, IT architectures, product models, multi-project plans § Software Engineering Qualities modularity, reusability, typing, binding, naming Sebis Research Profile © sebis 41
  42. 42. Spreadsheets 2.0: Analysis of complex linked data Hierarchical data structures Networks Bank Geschäft IT Unternehmens -steuerung Handel Kredit Andere Produkte Prozesse Anwendungen Infrastruktur Support Accounting Controlling Reporting Compliance For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de) Sebis Research Profile © sebis 42
  43. 43. Spreadsheets 2.0: Analysis of complex linked data 푓 푓 푓 푓 푓 푓 푓 푓 푓 푓 푓 Data Functions / Transformations Visualizations Users For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de) Sebis Research Profile © sebis 43
  44. 44. Spreadsheets 2.0: Analysis of complex linked data System vision § Hybrid Wiki data model § Transparency through pipes & filters architecture § Functional query language (à la LINQ, Scala, …) § Intuitive interactive web-based user experience § Fully integrated in collaboration environment § Optimized „real time“ evaluation Research questions § User interface concepts and design (data, functions, views)? § How do users work with historic data and time series? § Language design (DSL, familiarity ó expressiveness)? § System architecture and integration with emerging “big data” technologies? § Evaluation strategies? § Optimization strategies (materialized views, …)? For more information visit Spreadsheet 2.0 (http://wwwmatthes.in.tum.de) Sebis Research Profile © sebis 44
  45. 45. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 45
  46. 46. Information systems for problem solving Reproductive Thinking (Heuristics, Algorithms etc.) Puzzle Productive Thinking (Creativity etc.) Problem Wicked Problem Problem Example Information System Support Degree of Collaboration Degree of Automation Measuring temperature, … Business Model Generation, … Sensors, Embedded Systems, Robotics, Databases, … Accounting, … SAP R/3, Word Processing, Spreadsheet Software, … Collaborative Informationsystems, e.g. Wikis, Dropbox, … Sebis Research Profile © sebis 46
  47. 47. IS support for a complex problem: Business model generation • Re-use benefits of existing tools and methods • Business Model Canvas • Common terminology • Visual representation • Computer-Aided Morphological Analysis • Basic problem solving process structure • Interactive model of the problem/solution space • Clustering of similar business models • Multi-user support • Group facilitation support • Alternate between individual and collaborative phases è avoid social bias • Alternate between convergent and divergent phases è promote creativity • Alternate between anonymous and identified interactions è avoid social loafing, increase (constructive) social competition Work-in-progress: currently implementing prototype, designing process model Sebis Research Profile © sebis 47
  48. 48. Research projects and results 1. Enterprise Architecture Management § IT Architecture in Turbulent Times § Agile Enterprise Architecture Management § Quantitative Models in Enterprise Architecture Management § Federated Enterprise Architecture Model Management § CALM3: Complexity of Application Landscapes § Semantic Processing of Legal Texts for IT Compliance 2. Social Software Engineering § Darwin: Process Support for Collaborative Knowledge Work § Spreadsheets 2.0: Analysis of Complex Linked Data § Social Software for Complex Problem Solving § COLVA: Collaborative Learning Video Annotations Sebis Research Profile © sebis 48
  49. 49. Colva: Collaborative learning video annotations Motivation § Increasing amount of online learning / lecture / teaching / demonstration / knowledge / … videos § New players: universities, schools, individuals, non-profit organizations, businesses, media companies, … § It is difficult for learners and educators to discover new relevant material for a given topic § It is difficult for learners to find the exact location where a particular topic has been covered § Increase quality of the learners feedback on the education material and way of teaching Research questions § What are the inhibitors of the collaborative learning video annotations? § How the tool for collaborative learning video annotations effects the behavior of instructors and learners? Sebis Research Profile © sebis 49
  50. 50. Colva: Collaborative learning video annotations A conceptual framework for describing augmented teaching sessions Phases Preparation Live teaching session Post-processing Actors Instructor Learner Plan timing of teaching session Prepare teaching material. Present teaching material [Take or review notes.] Activity Plan timing of teaching session. ( verb) (nouns) activity content involved in the activity [Take or review notes.] (brackets) optional activities Sebis Research Profile © sebis 50
  51. 51. Colva: A collaborative learning video annotations Possible synchronous and asynchronous collaboration via video annotations Phases Preparation Live teaching session Post-processing Actors Instructor - View annotation. View and create annotation. Learner - Create and view annotation. Create and view annotation. Sebis Research Profile © sebis 51
  52. 52. Colva: Collaborative learning video annotations on the web Implementation stages Stage 1 Stage 2 Stage 3 Provide a web solution for collecting learners annotations during the learning session Synchronize video-recordings with collected real-time user annotations Test and evaluate different methods for collaboration through video annotations usage Pilot project Current objective Implement concept in viable prototype For more information contact Klym Shumaiev klym.shumaiev@tum.de Sebis Research Profile © sebis 52 “Wouldn’t it be nice, if you as a Bachelor student at the faculty of informatics at TU Munich could easily create and manage collaborative annotations aligned with video recordings of the lectures?” Who? How? What?
  53. 53. Thank you for your attention. Questions? Technische Universität München Department of Informatics Chair of Software Engineering for Business Information Systems Boltzmannstraße 3 85748 Garching bei München Tel +49.89.289. Fax +49.89.289.17136 wwwmatthes.in.tum.de Florian Matthes Prof.Dr.rer.nat. 17132 matthes@in.tum.de

×