to use Fuzzy Cognitive Map (FCM)

USING FUZZY COGNITIVE MAPS FOR SELECTING COMPLEMENTARY SUBJECTS IN A PROFESSIONAL ACCREDITATED MASTER – MASTER IN FORESTRY ENGINEERING

J. Solana-Gutiérrez, G. Rincón-Sanz, C. Alonso-González, M.P. Arraiza Bermudez-Cañete Escuela Técnica Superior de Ingenieros de Montes UNIVERSIDAD POLITÉCNICA DE MADRID Contact e-mail: [email protected] 1

9th International Technology, Education and Development Conference

Madrid – 2nd – 5th March 2015

Introduction • The Bologna Declaration has promoted the convergence of the European Higher Education Area (EHEA) and has stimulated the characterization of curricula based on the concepts of level of competition and European credits (ECTS), which has simplified the way to compare academic qualifications. • This synoptic characterization of the curriculum can also be used to facilitate professional accreditation agencies charged of analysing and validating academic degrees for professional practice. • Based on academic programs and ECTS some graph network analysis technics can be used to optimize syllabus and student curriculum, for instance Fuzzy Cognitive Maps (FCM). USING FUZZY COGNITIVE MAPS FOR SELECTING COMPLEMENTARY SUBJECTS IN A PROFESSIONAL ACCREDITATED MASTER – MASTER IN FORESTRY ENGINEERING

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Introduction • In twentieth century, the market for higher education had been a domestic market, however in recent years it turned on a wide open market. Student mobility within the EU has increased the heterogeneity of the technicians’ degrees. • Currently, public and private universities are charged of building higher education program. Each university independently manufactures its academic offers based on its demand and its availability of resources. • Thus a wide panoply of offers from different universities is producing a considerable heterogeneity on academic degrees that used to be quite similar


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Introduction • Sometimes academic degrees heterogeneity produces misunderstandings and inefficiency in selecting workers for job positions. • To avoid these negative effects, it should encourage the accreditation of qualifications and academics degrees based on objective, measurable and comparable concepts (Gonzalez and Wagenaar, 2006) according to the principles set down in the Lisbon Convention (Council of Europe, 1997) and Bologna Declaration (1999).


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Introduction

• Additionally, the working group on European Area of Recognition (TNUFFIC, 2014) has published an essential coordination in recognition of vocational training guide. • This guide emphasizes that the objectification of recognition of credits taken in other institutions must be based on detailed analysis of the educational programs, the specific content and learning outcomes.


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Objectives • Our proposal is to use Fuzzy Cognitive Map (FCM) to produce a map of knowledge where academic subject relationships are evaluating quasiquantitative based on the temporal and thematic sequence of the learning process measured in ETCS. • Fuzzy Cognitive Map (FCM) facilitates finding the deficit of knowledge to obtain a professional accreditation.

• The final objective is to select which optative academic subject is the best to be taken for a graduate in Natural Environment Engineering who are studying a Master in Mountain Engineering and he want to be acreditate as a forester


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Fuzzy Cognitive Maps (FCM) • Fuzzy Cognitive Maps (FCM) is a simple and easy way to model the relationship between academic subjects in a academic program. • FCM is a qualitative model that describes different aspects of the behavior of a complex system in terms of concepts and the causal relationships among them.


Fuzzy Cognitive Maps (FCM) A FCM is an oriented graph compound of: - Nodes=Academic subjects or concepts (C1, C2,...,Cn) used to describe system behavior. - Arrows=Transfers of knowledge between concepts (C1-C2, C1-C3).

Each relationship is evaluated with the number of ECTS transmitted or received C1 +

-

C2

C3 + USING FUZZY COGNITIVE MAPS FOR SELECTING COMPLEMENTARY SUBJECTS IN A PROFESSIONAL ACCREDITATED MASTER – MASTER IN FORESTRY ENGINEERING

Fuzzy Cognitive Maps (FCM) FCM Elements • State vector (A= a1, a2,...,an): represents the value of concepts, between 0 and 1. The dynamics of the state vector is the principal output of applying a Fuzzy Cognitive Map. • Adjacency matrix (Ec=eij): contains the values of all relationships between concepts (values between -1 and 1).

C1

C2

C3

C1

e11

e21

e31

C2

e21

e22

e32

C3

e31

e23

e33

Fuzzy Cognitive Maps (FCM) FCM Outcomes: – Out-degree: An indicator of knowledge transmission to other subjects. – In-degree: An indicator of knowledge receiver from other academic subjects. – Centrality: Out-degree + In-degree : It is an indicator of subject importance In-degree:

C2

Out-degree C1

FCM applied to Academic syllabus The analysis stages are: 1) The syllabus of Engineering in Range Mountains (IMONTES) of Technical University of Madrid (BOE, 1976) is breaking into thematic blocks measured in ECTS.


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FCM applied to Academic syllabus 2) Each thematic block is a piece of knowledge transferred to other academic subjects.


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FCM applied to Academic syllabus

3) At each node or academic subject is made a balance of knowledge received and transmitted. Algebra 0.4

0.1

Statistics

Climatology 0.5


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FCM applied to Academic syllabus 4) The ensemble of Natural Environment Engineering (GIMN) (BOE, 2007) and Master in Mountain Engineering (MAIM) (ANECA, 2012) were analysed with FCM.


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FCM applied to Academic syllabus 5) Integrated GIMN+MIFM outcome indexes

GIMN+MAIM In-degree Indicator 18.00 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00


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FCM applied to Academic syllabus 5) Integrated GIMN+MAIM outcome indexes 18.00

GIMN+MAIM Out-degree Indicator

16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00


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FCM applied to Academic syllabus 5) Integrated GIMN+MAIM outcome indexes GIMN+MAIM Centrality Indicator 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00

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FCM applied to Academic syllabus 6) A comparison between GIMN+MAIM and the former IMONTES syllabus was made, which facilitates the decision to select among the offering of optative academic subjects. Master in Mountain Engineering (MAIM) – Optative subjects Environment Engineering Remote sensing Biodiversity sampling Biotechnology Mathematics Forest economy Forest harvest Construction Selviculture Climate change & phyto-climatology Biogeochemical & climate change Soil classification Flora & habitats Land registry and land assessment Red Natura 2000 Sustainable harvesting Planning & project management

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Results • As a result of comparison between IMONTES FCMap to GIMN+MAIM FCMap, the latter presents a deficit of 6.14 academic subjects (73.68 credits) on technological subjects • However, it was found that potential GIMN plus MAIM graduates would have over studied 9 biological subjects with an effort of 2.82 ECTS

OVER STUDIED SUBJECTS Organic Chemistry and Biochemistry Anatomy and Plant Physiology Botany, Dendrology and Geobotany Surface Hydrology and Soil Conservation Meteorology and Plant Ecology Forest Mensuration Mechanics and Mechanisms General Hydraulics and applied Soil Science

0.49 0.48 0.43 0.38 0.27 0.21 0.21 0.15 0.12


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Results Thematic deficit are related to 31 academic subjects with an effort of 24.26 ECTS. Academic subjects with shortage of credits Electronic and Control Systems Principles of Economics General and Industrial Technology Wood Preservation Planning and Projects Wood Technology II Wood Technology I Technology Cork, Resins and Essential oils Chemistry of Non-Market Forestry Products Engines and Heating Engines Wood chemistry and technology of pulp and paper Electrical Engineering II Electrical Engineering I Forest Harvesting Forest Industries Construction I General Technology Forest Products

1.7 1.57 1.44 1.33 1.25 1.15 1.12 1.1 1.07 1.07 0.91 0.85 0.85 0.82 0.83 0.73 0.71


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Results Making a decision which balances between deficit and over studied subjects, student may choose the most advisable optative Master in Mountain Engineering (MAIM) – Optative subjects Environment Engineering Remote sensing Biodiversity sampling Biotechnology Mathematics Forest economy Construction Selviculture Climate change & Phyto-climatology Biogeochemical & climate change Soil classification Flora & habitats Land registry and land assessment Red Natura 2000 Sustainable harvesting Planning & project management

Deficit

Surplus

5.91

0.36

8.22

0.21 0.49

1.57 0.73 0.43 0.27 0.12 0.48

0.82 1.25

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Results • Advisable optative subjects are in the following order: Biotechnology, Environment engineering, Forest economy, Planning & Project management, Sustainable harvesting, and Construction

• Optative academic subjects related to new fields: Remote sensing, Mathematics, Selviculture, Land registry and land services assessment, and Red Natura 2000 • Optative subjects linked to over studied themes: Flora & habitats, Climate change & Phyto-climatology, Biogeochemical & Climate change, Biodiversity sampling, and Soil classification

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Conclusions -

FCM facilitates syllabus comparison between similar degrees. In this sense, FCM can be used to curricula assessment to achieve a professional accreditation.

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A high in-degree indicator subject is linked to a finalist academic subject, and a high out-degree is linked to an instrumental subject.

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In order to reach a professional accreditation as forester, graduates in Natural Environment Engineering should matriculate in the following optative subjects: 1)Biotechnology, 2)Environment engineering, 3)Forest economy, 4)Planning & project management, 5) Sustainable harvesting and 6) Construction


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THANKS FOR YOUR ATTENTION USING FUZZY COGNITIVE MAPS FOR SELECTING COMPLEMENTARY SUBJECTS IN A PROFESSIONAL ACCREDITATED MASTER – MASTER IN FORESTRY ENGINEERING J. Solana-Gutiérrez, G. Rincón-Sanz, C. Alonso-González, M.P. Arraiza Bermudez-Cañete Escuela Técnica Superior de Ingenieros de Montes UNIVERSIDAD POLITÉCNICA DE MADRID Contact e-mail: [email protected] A LEARNING BASED ON PROYECT EXPERIENCE IN A FORESTRY ENGINEERING 9th International Technology, Education and DevelopmentDEGREE Conference

Madrid – 2nd - 5th March 2015