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Contributions to the multidisciplinarity of
computer science and IS
« Contributions à la multidisciplinarité de
l’informat...
Agenda
 Preliminary thoughts (x4)
 Research works and research path
I. Epistemological analysis : a tentative
II. About ...
Preliminary thoughts (1st) 3
A seminar in the 90’ about “action research” …
Thought 1: Computing can be considered as a
ph...
Preliminary thoughts (2nd)
Programming would be an “artistic” activity?
4
… with no “scientific” basis?
Hoare, C. A. R. (1...
Preliminary thoughts (2nd) 5
“art” ≠ “artistic”
Skills ...
 Practice oriented skills (know how – way of doing)
 Methodol...
Preliminary thoughts (3rd)
Doing research about e-mail ?
6
… a 30 years old artifact …
• Usage problems
• Strong impact
Th...
Preliminary thoughts (4th) 7
Exclusivity of research methods in management,
social and organizational sciences?
(Int. Symp...
Preliminary thoughts … 8
Conclusion …
 Necessity of a holistic, epistemology based, conciliating analysis
… to clarify my...
Research works and research path 9
A framework for IS research
Source
Lyytinen, K. (1987). Different perspectives on infor...
Research works and research path 10
Topic 2
(IS usage and
impact)
ProAdmin eGov
project
2005-06
Integrating Learning
Style...
Research works and research path 11
Topic 2
(IS usage and
impact)
ProAdmin eGov
project
2005-06
Integrating Learning
Style...
Agenda
 Preliminary thoughts (x4)
 Research works and research path
I. Epistemological analysis : a tentative
II. About ...
I. Epistemological analysis : a tentative 13
Multidisciplinary questioning
 The subject of study [ontology]
 The investi...
To reason on artifacts capable of
concrete and abstract calculations
To understand and explain
phenomena related to the
pr...
II. About methods 15
Method(s) in formal sciences
Mathematical notations and reasoning
“Absolute validity” … as long as
...
II. About methods 16
Method(s) in factual sciences
Source
Bunge, M. (1967). Scientific Research 1: The Search for System (...
II. About methods 17
Method(s) in design – the roots
Brunelleschi (1377-1446)
“… recognized to be the first modern
enginee...
II. About methods 18
Product (i.e. artifact)
Process
Methods in design – product vs. process
Design is about both the prod...
II. About methods 19
Methods in design – link with science
Multiple terminology …
 Design studies
 Design discipline
 D...
II. About methods 20
Methods in design – “Design science” in IS and MIS
Source
Wieringa, R. (2009). Design science as nest...
II. About methods 21
My recent publications related to methodological
issues (2012 – 2016)
Topics tackled
‒ Method Enginee...
Agenda
 Preliminary thoughts (x4)
 Research works and research path
I. Epistemological analysis : a tentative
II. About ...
III. Research works 1: Model enactment 23
Modeling in IS design
Source
Wand, Y., & Weber, R. (1993). On the ontological
ex...
III. Research works 1: Model enactment 24
What is model enactment?
Direct execution / interpretation of process models by ...
III. Research works 1: Model enactment 25
RUBIS system (1986-1990)
RUBIS architecture
o Event processor
o Language interpr...
III. Research works 1: Model enactment 26
The MAP notation (1998 – today)
Intentional specification of a process (business...
III. Research works 1: Model enactment 27
A specific MAP engine
Enactment through a dedicated architecture
 Repository st...
III. Research works 1: Model enactment 28
Implicit vs. explicit semantics of modeling languages
Source
Mayerhofer, T., Lan...
III. Research works 1: Model enactment 29
A generic approach – an exploratory study
What about using meta-modeling tools?
...
III. Research works 1: Model enactment 30
A generic approach – an exploratory study
Results
 Feasible for the “structure”...
III. Research works 1: Model enactment 31
A generic approach – a proposal
Transformation
rules
Code
generation
CIM level P...
III. Research works 1: Model enactment 32
A generic approach – behavior meta-modeling
stopMapEnact()
selectCandidateSectio...
III. Research works 1: Model enactment 33
Conclusion
Practical problem
 Actual meta-modeling & CAME tools are limited
 A...
Agenda
 Preliminary thoughts (x4)
 Research works and research path
I. Epistemological analysis : a tentative
II. About ...
IV. Research works 2: exploiting defect text reports 35
Defects in software
Textual
description
IV. Research works 2: exploiting defect text reports 36
Defect lifecycle and resolution time prediction
How much time will...
IV. Research works 2: exploiting defect text reports 37
Prediction using text similarity (Weiss et al., 2007)
Previous def...
IV. Research works 2: exploiting defect text reports 38
Empirical SE, 18(1), 2013, pp. 117-138.
Defect Resolution Time (DR...
IV. Research works 2: exploiting defect text reports 39
Data preparation
Prediction simulation
Replication
The experimenta...
IV. Research works 2: exploiting defect text reports 40
The experimental study : replication
K-means clustering
(100% data...
IV. Research works 2: exploiting defect text reports 41
Prediction simulation
Data preparation
K-means clustering
(x% of t...
IV. Research works 2: exploiting defect text reports 42
The experimental study : testing the claim …
Three data sets | K=4...
IV. Research works 2: exploiting defect text reports 43
Three data sets | K=4 | Pred(0.25) | Number of test points (SSF) =...
IV. Research works 2: exploiting defect text reports 44
The experimental study : testing the claim …
Company A| K = 6 – 8 ...
IV. Research works 2: exploiting defect text reports
 Limited reliability of text based approaches to DRT prediction
 Ne...
Agenda
 Preliminary thoughts (x4)
 Research works and research path
I. Epistemological analysis : a tentative
II. About ...
V. Research works 3: learning style impact 47
 Individual differences in learning and
acquiring knowledge
 Over 70 learn...
V. Research works 3: learning style impact 48
LS and e-media integration (1) Felder-Silverman
LS modelA-L. Franzoni-Velázq...
V. Research works 3: learning style impact 49
 Delphi method with 20 participants (univ. teachers)
 Partial implementati...
V. Research works 3: learning style impact 50
 Kolb LS model
 Technology Acceptance Models (TAM,
UTAUT)
 LS as a modera...
V. Research works 3: learning style impact 51
Mobile learning usage and adoption (2)
Acceptance
 Empirical testing (39 va...
V. Research works 3: learning style impact 52
Mobile learning usage and adoption (3)
Continuance to use
 Empirical testin...
V. Research works 3: learning style impact 53
 Differences in learning certainly exist … are they correctly captured
in l...
Agenda
 Preliminary thoughts (x4)
 Research works and research path
I. Epistemological analysis : a tentative
II. About ...
VI. Conclusion
 An apparent heterogeneous collection of research works
… yet, IT artifact design and usage are at the cen...
VI. Conclusion
 Design and usage of DSL for new technologies (IoT ?)
 Enterprise modeling for Digital Transformation
 T...
VI. Concluding remarks 57
Source
Ramsin, R., The Engineering of an Object-Oriented Software Development Methodology, PhD t...
VI. Concluding remarks 58
About methods: multidisciplinary misunderstandings?
Matthieu Cisel, Utilisation des MOOC : éléme...
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Contributions to the multidisciplinarity of computer science and IS

Les diapos de ma présentation HDR en informatique (CNU section 27) à l'université Paris 1 Panthéon Sorbonne le vendredi 20 janvier 2017. L'enregistrement vidéo de la soutenance est visible sur https://www.youtube.com/watch?v=1ro_iaI-roA
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Slides of my presentation for Habilitation (HDR) defense in computer science (Informatique section 27 CNU) at University Paris 1 Panthéon Sorbonne on Friday January 2017.
Video recording is visible on https://www.youtube.com/watch?v=1ro_iaI-roA

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Contributions to the multidisciplinarity of computer science and IS

  1. 1. Contributions to the multidisciplinarity of computer science and IS « Contributions à la multidisciplinarité de l’informatique et des SI » Habilitation à Diriger les Recherches 20 janvier 2017 Saïd Assar Institut Mines Telecom, Ecole de Management
  2. 2. Agenda  Preliminary thoughts (x4)  Research works and research path I. Epistemological analysis : a tentative II. About methods III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning context VI. Conclusion 2 1st period 2nd period 3rd period
  3. 3. Preliminary thoughts (1st) 3 A seminar in the 90’ about “action research” … Thought 1: Computing can be considered as a phenomena, as a social subject of study
  4. 4. Preliminary thoughts (2nd) Programming would be an “artistic” activity? 4 … with no “scientific” basis? Hoare, C. A. R. (1984). Programming: Sorcery or Science? IEEE Software, 1(2), 5-16.
  5. 5. Preliminary thoughts (2nd) 5 “art” ≠ “artistic” Skills ...  Practice oriented skills (know how – way of doing)  Methodological skills Thought 2: Writing computer programs is a design issue (vs. programs as formal subjects)
  6. 6. Preliminary thoughts (3rd) Doing research about e-mail ? 6 … a 30 years old artifact … • Usage problems • Strong impact Thought 3: Relevance is not necessarily related to the novelty of the artifact New requirements …
  7. 7. Preliminary thoughts (4th) 7 Exclusivity of research methods in management, social and organizational sciences? (Int. Symposium on Empirical SE, 2004) (Int. Symposium on Empirical SE and Measurement, 2009) (Information and Software Tech., 2014) (Int. Conference on SE – ICSE, 2004) … not really (Empirical Softw. Eng., 2011) B. Kitchenham Thought 4: Differences among disciplines is not always related to research methods
  8. 8. Preliminary thoughts … 8 Conclusion …  Necessity of a holistic, epistemology based, conciliating analysis … to clarify my research path and position my contributions o Quid the disciplinary differences in terms of scientific contributions? o Quid the differences in terms of validity and veracity? o Quid the different meanings and understandings of the idiom “method”? o Quid of  IS Engineering  Software Eng.  Management IS  Empirical SE
  9. 9. Research works and research path 9 A framework for IS research Source Lyytinen, K. (1987). Different perspectives on information systems: problems and solutions. ACM Computing Surveys, 19(1), 5–46. Topic 2 Topic 1
  10. 10. Research works and research path 10 Topic 2 (IS usage and impact) ProAdmin eGov project 2005-06 Integrating Learning Styles in eLearning 2005-09 eGov evolution analysis 2007-14 LS impact on Mobile Learning 2012-14 A global view of my research activity Sabbatical at LTH & empirical SE 2012 - 15 Topic 1 (IS design and development) Model transf. & code generation 1995 Map engine design 2002-05 Traceability meta-modeling 2007-09 Intentional service desc. & discovery 2009-11 Generic approach to model enactment 2008-14 Topic 1 Topic 2
  11. 11. Research works and research path 11 Topic 2 (IS usage and impact) ProAdmin eGov project 2005-06 Integrating Learning Styles in eLearning 2005-09 eGov evolution analysis 2007-14 LS impact on Mobile Learning 2012-14 Sabbatical at LTH & empirical SE 2012 - 15 Topic 1 (IS design and development) Model transf. & code generation 1995 Map engine design 2002-05 Traceability meta-modeling 2007-09 Intentional service desc. & discovery 2009-11 Generic approach to model enactment 2008-14 Works presented III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning
  12. 12. Agenda  Preliminary thoughts (x4)  Research works and research path I. Epistemological analysis : a tentative II. About methods III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning context VI. Conclusion 12 2nd period
  13. 13. I. Epistemological analysis : a tentative 13 Multidisciplinary questioning  The subject of study [ontology]  The investigation method [methodology] Epistemology … Mario Bunge
  14. 14. To reason on artifacts capable of concrete and abstract calculations To understand and explain phenomena related to the production and usage of artifacts To build useful artifacts that fit with usage context “L’informatique” Science of the artificial I. Epistemological analysis : a tentative Formal science Factual science 14 Ontological perspective
  15. 15. II. About methods 15 Method(s) in formal sciences Mathematical notations and reasoning “Absolute validity” … as long as  rules of logic and deduction are respected  assumptions and hypotheses are holding The “mathematisation” of a problem …  a powerful approach to research  positively perceived in general
  16. 16. II. About methods 16 Method(s) in factual sciences Source Bunge, M. (1967). Scientific Research 1: The Search for System (Vol. I). New York: Springer, (page 9) Validity ? Contribution ?
  17. 17. II. About methods 17 Method(s) in design – the roots Brunelleschi (1377-1446) “… recognized to be the first modern engineer …” [Wikipedia] Designeo (It) Dessein (Fr) Dessin (Fr)Design (En) To design is “to draw intentions”  Modeling is the language of design  Design seeks the production artifacts …  What about the production of knowledge ?
  18. 18. II. About methods 18 Product (i.e. artifact) Process Methods in design – product vs. process Design is about both the product and the process …  Knowledge about the product or the process ?  Idiographic (specific) vs. Nomothetic (generic) knowledge ?  Process guidance ?
  19. 19. II. About methods 19 Methods in design – link with science Multiple terminology …  Design studies  Design discipline  Design research  Scientific design  Design science … Design vs. science  Design-based learning and discovery [Source : Nigel Cross, “Designerly way of knowing”, 1982 / 2001]
  20. 20. II. About methods 20 Methods in design – “Design science” in IS and MIS Source Wieringa, R. (2009). Design science as nested problem solving. In Proceedings of the 4th Int. Conf. on Design Science Research in IS and Technology (DESRIST’09), New York, USA: ACM.  Epistemological necessity of distinguishing “practical” and “knowledge” problems
  21. 21. II. About methods 21 My recent publications related to methodological issues (2012 – 2016) Topics tackled ‒ Method Engineering [a synthesis] ‒ MDE and Requirements Engineering [a review of ModRE workshops, 2011-13] ‒ Creative approaches in RE [a state of the art ] ‒ Replication in experimental research [experimental study] ‒ Methods for literature review [a synthesis] ‒ Empirical and experimental methods in IS eng. [a synthesis] ‒ Theory development in ERP research [exploratory study]
  22. 22. Agenda  Preliminary thoughts (x4)  Research works and research path I. Epistemological analysis : a tentative II. About methods III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning context VI. Conclusion 22 3rd period
  23. 23. III. Research works 1: Model enactment 23 Modeling in IS design Source Wand, Y., & Weber, R. (1993). On the ontological expressiveness of information systems analysis and design grammars. Information Systems Journal, 3(4), 217–237. Source Rolland, C., & Prakash, N. (2000). From conceptual modelling to requirements engineering. Annals of Software Engineering, 10(1-4), 151-176.  Conceptual gap: requirements vs. systems ?  Mapping ?  Transformation ?  (Semi-) automated ?  Data vs. process models ?  Business vs. engineering processes?
  24. 24. III. Research works 1: Model enactment 24 What is model enactment? Direct execution / interpretation of process models by a software agent: o Prototyping o Model validation o Higher level of abstraction Software agent Model(s)
  25. 25. III. Research works 1: Model enactment 25 RUBIS system (1986-1990) RUBIS architecture o Event processor o Language interpreter o Forms generation Limitations o Implicit semantics o Product dependant o Model specific (i.e. Remora)
  26. 26. III. Research works 1: Model enactment 26 The MAP notation (1998 – today) Intentional specification of a process (business or engineering process)  Process realization through multiples intentions  Intention realization through multiple strategies Informal, complex semantics  Selection of a “candidate section”  Execution of a strategy  Product and process trace
  27. 27. III. Research works 1: Model enactment 27 A specific MAP engine Enactment through a dedicated architecture  Repository structure derived from the meta-model  Strategies semi-formally specified  Specification transformed into code Limitations o Tentative for making semantics explicit o Product dependant o Model specific (i.e. MAP) M-H. Edme. « Proposition pour la modélisation intentionnelle et le guidage de l’usage des systèmes d’information ». PhD, Université Paris 1, 2005.
  28. 28. III. Research works 1: Model enactment 28 Implicit vs. explicit semantics of modeling languages Source Mayerhofer, T., Langer, P., Wimmer, M., & Kappel, G. (2013). xMOF: Executable DSMLs Based on fUML. In M. Erwig et al. (Eds.), Software Language Engineering (SLE’13), p. 56-75, Springer.  Implicit semantics generate redundancy and incoherence  Explicit semantics would enable the application of “MDE techniques for processing language [meta-] specifications”
  29. 29. III. Research works 1: Model enactment 29 A generic approach – an exploratory study What about using meta-modeling tools?  Based on meta-modeling  Automatic generation of a CASE tool  State of the art : o Structure meta-modeling o Limited / absent behavior meta-modeling o Difficulty in handling multiple levels of instantiations
  30. 30. III. Research works 1: Model enactment 30 A generic approach – an exploratory study Results  Feasible for the “structure” part of a meta-model (i.e. MAP editor)  Semantics specified in the code generation scripts (i.e. not explicit) => Limitation in the meta-modeling languages
  31. 31. III. Research works 1: Model enactment 31 A generic approach – a proposal Transformation rules Code generation CIM level PIM level (XML specifications) PSM level Abstract syntax Semantics Engine architecture Structural view Static meta-model (UML) Behavioral view Dynamic meta-model (Remora) Process enactment engine S. Mallouli. « Méta-modélisation du Comportement d’un Modèle de Processus: Une Démarche de Construction d’un Moteur d’Exécution ». PhD thesis, Univ. Panthéon-Sorbonne-Paris I, 2014.
  32. 32. III. Research works 1: Model enactment 32 A generic approach – behavior meta-modeling stopMapEnact() selectCandidateSection() stateMapInst.Old=Selected stateMapInst.New=Running M1: Start Map Execution notifyEndSection() SectionInstance Product Instance SendCandidateSections() EV4 EV5 EV9 MapActor M2:Liste of candidates M4:End of Section Execution compute Candidate Sections() IntentionInstance Section Application Executor updateIntention() updateExecSection() notifyEndExec() invokeExec() EV1 M3: Choice C1: target intention= Stop C1 MapInstance startMapEnact() stateMapInst.Old=Running stateMapInst.New=Enacted notifyEndMapEnact() M6:Stop stateSecInst.Old=Candidate statSecInst.New=Selected M8:Execution status computeCandidateSections() stateSecInst.Old=Created stateSecInst.New=Candidate stateSecInst.Old=Selected stateSecInst.New=Executed EV3 EV6 EV8 EV12 Product Manager ImplemExe SectionInstance invokeExec() notifyEndExec() EV5 EV10 stateIntInst.Old=Created stateIntInst.New=Realised notifyProduct() EV11 updateProduct() M5: Product M9: NewProduct Situation EV7 M7:Execution ParametersstateImpl.Old=Created stateImpl.New=Selected C2 C2: target intention=/= Stop newProductInstance() EV2 M1bis: Start MapInst newMapInstance() F1 F1: pour toute sectionInstance retournée par l'algorithme de calcul de candidates
  33. 33. III. Research works 1: Model enactment 33 Conclusion Practical problem  Actual meta-modeling & CAME tools are limited  A proposal for the graphical expression of semantics  Operational semantics = the architecture of an enactment engine Knowledge problems  feasibility (conceptual – technical – practical)  Scalability MAP for method engineering  a very powerful conceptual tool  .. yet difficult to enact in a generic manner (product and process inseparability)
  34. 34. Agenda  Preliminary thoughts (x4)  Research works and research path I. Epistemological analysis : a tentative II. About methods III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning context VI. Conclusion 34 3rd period
  35. 35. IV. Research works 2: exploiting defect text reports 35 Defects in software Textual description
  36. 36. IV. Research works 2: exploiting defect text reports 36 Defect lifecycle and resolution time prediction How much time will it take to fix? => As much as “similar” defects …
  37. 37. IV. Research works 2: exploiting defect text reports 37 Prediction using text similarity (Weiss et al., 2007) Previous defects Prediction set (K) New incoming defect Similarity level (α) Defect Resolution Time (DRT) prediction – State of the art Results  Good predictions when (α) is high … but limited applicability  Mostly, ~20% of acceptable predictions  No optimal value for parameters (α) and (K)
  38. 38. IV. Research works 2: exploiting defect text reports 38 Empirical SE, 18(1), 2013, pp. 117-138. Defect Resolution Time (DRT) prediction – State of the art Complete set of defect reports cluster1 cluster2 cluster3 cluster4 For all K,J MRTK and MRTJ are significantly different (ANOVA statistic test) MRTK = Mean Resolution Time for all defects in cluster K Clustering for DRT prediction ?
  39. 39. IV. Research works 2: exploiting defect text reports 39 Data preparation Prediction simulation Replication The experimental study S. Assar, M. Borg, D. Pfahl. “Using Text Clustering to Predict Defect Resolution Time: A Conceptual Replication and an Evaluation of Prediction Accuracy”. Empirical Software Engineering 22, no 3 (2016): 1-39.
  40. 40. IV. Research works 2: exploiting defect text reports 40 The experimental study : replication K-means clustering (100% data set) Statistical analysis (ANOVA & post-hoc test) Replication Data preparation Replication conditions ‒ Different data sets : 2 open source + 1 proprietary ‒ Different text mining tool : RapidMiner ‒ (Slightly) different data preparation steps Result Fully positive
  41. 41. IV. Research works 2: exploiting defect text reports 41 Prediction simulation Data preparation K-means clustering (x% of the data set) Analysis of predictive power x=10,20, …, 100% Statistical analysis The experimental study : testing the claim … Parameters of the experiment ‒ Data sets: Eclipse, Android, Company A ‒ Prediction error: 25% and 50% ‒ Size of the test set: 10% to 100% ‒ Number of defects used for testing: 1%, 3% and 5% ‒ Number of clusters: 4, 6, 8, 10
  42. 42. IV. Research works 2: exploiting defect text reports 42 The experimental study : testing the claim … Three data sets | K=4 | Pred(0.25) | Number of test points = 1% | Naïve prediction
  43. 43. IV. Research works 2: exploiting defect text reports 43 Three data sets | K=4 | Pred(0.25) | Number of test points (SSF) = 1% - 3% - 5% The experimental study : testing the claim …
  44. 44. IV. Research works 2: exploiting defect text reports 44 The experimental study : testing the claim … Company A| K = 6 – 8 – 10 | Pred(0.25) | Number of test points (SSF) = 3% - 5% Final result Claim not confirmed
  45. 45. IV. Research works 2: exploiting defect text reports  Limited reliability of text based approaches to DRT prediction  Need to challenge the theoretical grounding (i.e. “similarity assumption(s)”)  Knowledge production in a “factual science” manner with an empirical and inductive approach : – Replication is an essential (yet challenging) issue … – Validity depends on o Design of the experiment o Size and quality of the data o Sophistication and validity of data analysis procedures o Underlying theory 45 Conclusion
  46. 46. Agenda  Preliminary thoughts (x4)  Research works and research path I. Epistemological analysis : a tentative II. About methods III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning context VI. Conclusion 46 3rd period
  47. 47. V. Research works 3: learning style impact 47  Individual differences in learning and acquiring knowledge  Over 70 learning style models  Differences in Ls are easier to accommodate in IT based teaching and learning Problematic  Contradictory results concerning the impact of LS  How to exploit electronic media potentialities in an IT based learning context
  48. 48. V. Research works 3: learning style impact 48 LS and e-media integration (1) Felder-Silverman LS modelA-L. Franzoni-Velázquez, “A Proposed Method for Adapting and Integrating Student Learning Style, Teaching Strategies and Electronic Media”. PhD thesis, 2009.
  49. 49. V. Research works 3: learning style impact 49  Delphi method with 20 participants (univ. teachers)  Partial implementation  Test with ~700 students  Positive correlation LS and e-media integration (2)
  50. 50. V. Research works 3: learning style impact 50  Kolb LS model  Technology Acceptance Models (TAM, UTAUT)  LS as a moderating factor  Distinction between “acceptance” and “continuance to use”  Multiple experimentations Mobile learning usage and adoption (1) Yaneli Cruz, “Learning Styles Effect on Mobile Learning Acceptance: A Continuance Intention Approach” PhD thesis, 2014.
  51. 51. V. Research works 3: learning style impact 51 Mobile learning usage and adoption (2) Acceptance  Empirical testing (39 valid responses)  LS moderation effect is modest
  52. 52. V. Research works 3: learning style impact 52 Mobile learning usage and adoption (3) Continuance to use  Empirical testing (51 valid responses)  “Effort expectancy” and “Social influence” are the only variables that are moderated by users’ LS
  53. 53. V. Research works 3: learning style impact 53  Differences in learning certainly exist … are they correctly captured in learning styles?  Complex typology for e-media … that is highly cited  Knowledge production in a “factual science” manner and the hypothetico-deductive approach – Validity depends on o Underlying theory o Size and quality of the data o Design of the experiment o Sophistication and validity of data analysis procedures Conclusion
  54. 54. Agenda  Preliminary thoughts (x4)  Research works and research path I. Epistemological analysis : a tentative II. About methods III. Research works 1: Model enactment IV. Research works 2: Defect resolution time prediction V. Research works 3: Learning style impact in e-learning context VI. Conclusion 54
  55. 55. VI. Conclusion  An apparent heterogeneous collection of research works … yet, IT artifact design and usage are at the center  Importance of understanding “what we know” and “how we know it” – Learning about methods … – Research methods are an important facet of multidisciplinary research 55 So what ?
  56. 56. VI. Conclusion  Design and usage of DSL for new technologies (IoT ?)  Enterprise modeling for Digital Transformation  Text mining in Software Engineering  Theory development – In MIS research – In Design Science research  Meta-analysis for evidence aggregation 56 Research perspectives
  57. 57. VI. Concluding remarks 57 Source Ramsin, R., The Engineering of an Object-Oriented Software Development Methodology, PhD thesis, University of York, UK, 2006. About methods : terminological / conceptual confusion?
  58. 58. VI. Concluding remarks 58 About methods: multidisciplinary misunderstandings? Matthieu Cisel, Utilisation des MOOC : éléments de typologie, Thèse de Doctorat, ENS Cachan, 08 juillet 2016  Description vs. Explanation … ?  Theoretical contribution … ?
  • JeromeArailletITILPS

    Feb. 13, 2017

Les diapos de ma présentation HDR en informatique (CNU section 27) à l'université Paris 1 Panthéon Sorbonne le vendredi 20 janvier 2017. L'enregistrement vidéo de la soutenance est visible sur https://www.youtube.com/watch?v=1ro_iaI-roA -- Slides of my presentation for Habilitation (HDR) defense in computer science (Informatique section 27 CNU) at University Paris 1 Panthéon Sorbonne on Friday January 2017. Video recording is visible on https://www.youtube.com/watch?v=1ro_iaI-roA

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