Fremtidens Datalogiske uddannelser

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1 Fremtidens Datalogiske uddannelser Bent Thomsen "Study the past if you would define the future..." Confucius

2 Datalogiske Uddannelser 2016 Kandidat Cand.scient. i datalogi DAT7 10 Cand.polyt. i software SW7 10 Cand.scient. i datalogi og tilvalgsfag DAT10 DAT6 Tilvalg Cand.scient. i datalogi og tilvalgsfag DAT10 Tilvalg DAT5 6 Cand.scient. i datalogi DAT9 10 DAT5 6 Cand.scient. i interaktionsdesign (fra 2017) IxD7 10 Cand.scient. i informatik INF7 10 Cand.scient. i datalogi (it) IT9 10 IT7 8 K+F+T: Cand.it i Interaktive Digitale Medier + Cand.mag i Informationsvidenskab F: Cand.polyt i Værdikæder og Innovationsledelse F+K: Cand.it i it ledelse Interaktionsdesign (fra 2017): Cand.polyt. i sustainable design Tilvalg Tilvalg T F K Bachelor DAT1 6 SW1 6 DAT1 5 DAT1 4 IxD1 6 INF1 6 BAIT5 Datalogi Datalogi og tilvalgsfag Datalogi og tilvalgsfag Interaktionsdesign Informatik Informationsteknologi Tilvalg = tilvalgsfag i naturvidenskab, humaniora eller samfundsvidenskab (tilvalg i idræt, humaniora eller samfundsvidenskab giver studietidsforlængelse på et halvt år (et semester))

3 Datalogiske Uddannelser 2016 De fleste uddannelser er designet i 2009 og indført i E2010 Små justeringer undervejs Følger 3*5+15 modellen Spareøvelse!! Ønske om flere kurser/aktiviteter på eksamensbeviset Obligatorisk valgfrihed på minimum 10 ECTS (kun på kurser!) Ingen formel sammenhæng mellem kurser og projekter Alignment med Bologna Kurser a 5 ECTS ~150 timers indsats for den studerende Honoreres med *n timer (200 timer + hjælpelærer for hver 50) ~ 12 forelæsning + miniprojekt/workshop/ Project a 15 ECTS Honoreres med 15 timer pr. Stud ~ 90 timer pr. gruppe Erstattede 2*3+2*3+18 modellen 2 SE og 2 PE kurser Kurser a 3 ECTS med 210 timer ~ 15 forelæsning/øvelser sessioner Project a 18 ECTS ~100 timer pr. gruppe

4 Ingeniør bachelor Uddannelsen før 2010 og efter 2010

5 Ingeniør kandidat Uddannelsen før og efter 2010

6 Datalogi bachelor og kandidat efter 2010

7 Undersøgelse af SW uddannelsen Assessing Problem Based Learning in a Engineering Curriculum Using Bloom s Taxonomy and the IEEE Engineering Body of Knowledge PETER DOLOG, LONE LETH THOMSEN, and BENT THOMSEN Trans. Comput. Educ. 16, 3, Article 9 (May 2016), 41 pages. DOI= Analyze competence levels according to Bloom s taxonomy assignment of Bloom taxonomy levels based on evidence found in the study regulation or gathered from all projects Analyzed all semester projects from the fourth semester (SW4), seventh semester (SW7), and 10th semester (SW10) It is not an assessment of individual students Compared with IEEE Engineering Body of Knowledge (SWEBOK) expected level for person with 4 year SW degree program and 4 years of experience Compared with the Graduate Engineering 2009 Curriculum Guidelines for Graduate Degree Programmes in Engineering (GSwE2009) Preliminary evaluation of consequences of change from old to new Aalborg model new = 3*5+15 model

8 Engineering Body of Knowledge SWEBOK Requirements Design Construction Test Maintenance Configuration Management Engineering Management Engineering Process Engineering Tools & Methods Quality Requirements Engineering Process Requirements Elicitation Requirements Analysis Requirements Specification Requirements Validation Requirements Management Design Basic Concepts Key Issues in Design Structure and Architecture Design Quality analysis and Evaluation Design Notations Design Strategies and Methods Reducation in Complexity Linguistic Methods Formal Methods Visual Methods Anticipation of Diversity Linguistic Methods Formal Methods Visual Methods Structuring for validation Linguistic Methods Formal Methods Visual Methods Use of External Standards Linguistic Methods Formal Methods Visual Methods Basic Concepts and Definitions Test Levels Test Techniques Test-related Measures Managing the Test Process Basic Concepts Maintenance Process Key issues In Maintenance Techniques for Maintenance Management of the SCM Process Configuration Identfication Configuration Control Configuration Status Accounting Configuration Auditing Release Management and Delivery Orgaqnizational Management Process/Project Management Engineering Measurement Engineering Process Concepts Process Infrastructure Process Measurement Process Definition Qualitative Process Analysis Process Implementation and Change Tools Requirements Tools Design tools Construction tools Testing tools Maintenance tools Engineering Process Tools Quality tools Congifuration Management Tools Engineering Management Tools Infrastructure Support Tools Miscellaneous tool Issues Methods Quality Concepts Definition & Planning for Quality Techniques Requiring 2 or more People Support to other Techniques Testing Special To SQA or V&V Defect Finding Techniques Measurement in Quality Analysis Heuristic Methods Formal Methods Prototyping Methods Miscellaneous method Issues

9 GSwE2009 vs. AAU

10 Overall conclusions engineering students at Aalborg University achieve higher levels of competencies in several areas software requirements fundamentals, software design, software construction (Application vs. Analysis/Evaluation) These higher competence levels are mainly achieved through the project work A few areas are at par or below software testing, no significant differences in comparison to the SWEBOK software quality, requirements management and change management SWEBOK: application level, AAU SW: comprehension Requires work in/with organizations of some maturity to achieve higher levels or longer running interaction with customers Compared to GSwE2009, only few areas need attention social, legal, and historical issues data confidentiality, security, surveillance, and privacy In addition students achieve enhanced transferable skills project management, communication, negotiation, and conflict resolution. Well known skills obtained through group based problem based learning. Analyzing grades for master s thesis projects, students at Aalborg University seem to perform above average. We attribute this, in part, to the fact that students at Aalborg University accumulate a lot of experience in uncovering real problems and finding good solutions, as well as plenty of experience in managing and executing projects.

11 PBL effects on grades

12 Konsekvenser af 3*5+15 modellen Konsekvenser for Projekt arbejde Mindre teori i rapporter Mindre reflekterende (og dermed lavere score på Bloom s taxonomi) Konsekvenser for kursus undervisning Mere focus på kurser (inkl. Tre eksamener) Masser af miniprojekter (mere PBL i kurser?) Brug af workshops Øvelser i plenum Brug af quizzes Video understøttelse af svære elementer Brug af eksternt producerede video materiale Flipped classroom

13 Mine konklusioner Vi har et godt product Vi skal være varsomme med radikale forandringer Selvfølgelig kan der laves forbedringer Selvfølgelig skal der laves forandringer

14 Tilbage til fremtiden Forslag om afskaffelse af Lineær algebra og flytning af diskret mat. Diskret mat på 1. semester + statistik på 2. semester Dækning af underrepræsenterede emner fra SWEBOK/GSwE2009? data confidentiality, security, surveillance, and privacy Nye trends imainstream: Parallelism Functional programming Formal methods Programmering i folkeskolen og i fritiden (hour of code, coding pirates) Større forskel på DAT og SW bachelor? Forskel på de studerendes selvopfattelse og opførsel allerede på 3./4. semester

15 SMAC the fifth era of computing After the mainframe era, mini computing era, personal computer and client server era, and the web era comes: SMAC Social, Mobile, Analytics and Cloud technologies as a Service (SaaS) Infrastructure as a Service (IaaS) Data Analytics/Big Data Additional topics: automation and robotics the IoT, cybersecurity agile and DevOps DevOps is a cultural shift and collaboration between development and operations strategic planning, innovation management, enterprise architecture, program management, and change management

16 Konsekvenser af regeringens spareprogram Normer reduceret fra E *n for kurser ~ en forelæsning mindre pr. Kursus 14.5 timer pr. Stud til vejledning ~ 1 2 vejledermøder færre Besparelser på m2 Ingen grupperum til pre speciale og speciale studerende Gruppearbejde i storrum Virtuelt gruppearbejde? Individuelt projektarbejde? Kan disse besparelser gøres til pædagogiske virkemidler? Brug af eksternt produceret materiale i standard kurser (MOOC)? 2* model eller 2* eller?

17 Diskussion Jeg maner til forsigtighed, men skal vi være mere radikale? Større forskel på DAT og SW Bachelor? SMAC skalden nye era tænkes mere gennemgribende ind? F.eks. På SW7/SW8 Hvordan kan vi opretholde kvaliteten i vores uddannelser med de nye økonomiske rammer? Kan/skal det tænkes ind i studieordningerne? Data Science erder brug for det i Nordjylland?

18 Data Science Conclusions from 2015 Reaseach Evaluation: Furthermore, society needs people that know how to handle data, so it is important to educate students in Data Science. The recommendation is to start a Data Science programme as soon as possible. What should be in a Data Science curriculum? Mathematics and statistics Programming Parallel and distributed systems Databases MapReduce and no SQL systems Machine intelligence Visualization of Data