2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

The corporation of the 1990s: Information technology and organizational transformation

ISO 3166-3 Newsletters are issued by the secretariat of the ISO 3166/MA when changes in the code lists of ISO 3166-3 have been decided upon by the ISO 3166/MA. ISO 3166-3 Newsletters are identified by a two-component number, stating the currently valid edition of ISO 3166-3 in Roman numerals (e g "I") and a consecutive order number (in Latin numerals) starting with "1" for each new edition of ISO 3166-3. The headers of the table below refer to the list part of ISO 31663:1999.

ISO (International Organization for Standardization ; 国際標準化機構)

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Learning Together: Peer Tutoring in Higher Education.

Mobile game-based learning constitutes a hot issue in the related scientific literature since it promotes learning through an entertaining way and fosters student motivation to increase engagement in the educational process. As such, it can enhance the learning process and improve student participation. Towards this direction, this paper investigates how mobile learning and game-based learning can be utilized in higher education settings and analyzes the pedagogical affordance of their adoption. As a testbed for our research, we designed and implemented Quiz Time! which is an intelligent mobile game-based learning application for assessing and advancing learners' knowledge in the programming language C#. Quiz Time! employs an assessing knowledge module for testing the knowledge of learners, a vectorial-based recommendation module for proposing personalized collaboration in group playing, a dynamic fuzzy logic-based advice generator for tailored assistance to learners' profile and misconceptions, and a cognitive learner modeler supporting the aforementioned modules. Quiz Time! was used in a higher education institution for an academic semester and was evaluated by students and computer science experts using an established framework and the statistical hypothesis test. Regarding the evaluation results, the computer science experts validated the pedagogical adequacy of the application and the students highlighted its positive impact on learning and its usefulness. A major conclusion is that incorporating personalization and collaboration in mobile game-based learning can further assist students in higher education towards advancing their knowledge level.

Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education

http://hypatia.teiath.gr/xmlui/bitstream/handle/11400/10440/Improving%20Quality%20of%20Educational%20Processes.pdf?sequence=1

Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques

A deep learning classification framework for early prediction of team-based academic performance

Mobile personalized learning can be achieved by the identification of students' learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires' choices, and thus, erroneous adaptation to students' needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires' choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students' learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students' learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.

https://www.mdpi.com/1099-4300/22/7/735/pdf

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

/pdf/exploration-of-augmented-reality-in-spatial-abilities-6clxys2vec.pdf

Cleo Sgouropoulou

Papers

Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education

Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques

A deep learning classification framework for early prediction of team-based academic performance

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Exploration of Augmented Reality in Spatial Abilities Training: A Systematic Literature Review for the Last Decade