Proceedings of the 12th European Conference on e ...

Proceedings of the 12th European Conference on e Learning e-Learning SKEMA Business School Sophia Antipolis France 30-31 October 2013 Volume One

Edited by Mélanie Ciussi and Marc Augier

A conference managed by ACPI, UK www.academic-conferences.org

Proceedings of the 12th European Conference on e-Learning ECEL 2013 SKEMA Business School Sophia Antipolis, France 30-31 October 2013 Edited by Mélanie Ciussi and Marc Augier

Copyright The Authors, 2013. All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors. Papers have been double-blind peer reviewed before final submission to the conference. Initially, paper abstracts were read and selected by the conference panel for submission as possible papers for the conference. Many thanks to the reviewers who helped ensure the quality of the full papers. These Conference Proceedings have been submitted to Thomson ISI for indexing. Please note that the process of indexing can take up to a year to complete. Further copies of this book and previous year’s proceedings can be purchased from http://academic-bookshop.com E-Book ISBN: 978-1-909507-84-5 E-Book ISSN: 2048-8645 Book version ISBN: 978-1-909507-82-1 Book Version ISSN: 2048-8637 CD Version ISBN: 978-1-909507-85-2 CD Version ISSN: 2048-8637

Published by Academic Conferences and Publishing International Limited Reading UK 44-118-972-4148 www.academic-publishing.org

Contents Paper Title

Author(s)

Page No.

Preface

v

Committee

vi

Biographies

ix

When Computers Will Replace Teachers and Counsellors: Heaven and Hell Scenarios

Aharon (Roni) Aviram and Yoav Armony

1

Planning and Implementing a new Assessment Strategy Using an e-Learning Platform

Rosalina Babo and Ana Azevedo

8

Authentic Learning in Online Environments – Transforming Practice by Capturing Digital Moments

Wendy Barber, Stacey Taylor and Sylvia Buchanan

17

Signature Based Credentials, an Alternative Method for Validating Student Access in e-Learning Systems

Orlando Belo, Paulo Monsanto and Anália Lourenço

24

Two-way Impact: Institutional e-Learning Policy/Educator Practices in Creative Arts Through ePortfolio Creation

Diana Blom, Jennifer Rowley, Dawn Bennett, Matthew Hitchcock and Peter Dunbar-Hall

33

Automated Evaluation Results Analysis With Data Mining Algorithms

Farida Bouarab-Dahmani and Razika Tahi

41

Language e-Learning Based on Adaptive Decision-Making System

48

Barriers Engaging With Second Life: Podiatry Students Development of Clinical Decision Making

Margaret Bruce, Sally Abey, Phyllis Waldron and Mark Pannell

58

Tasks for Teaching Scientific Approach Using the Black Box Method

Martin Cápay and Martin Magdin

64

Blended Learning as a Means to Enhance Students’ Motivation and to Improve Self-Governed Learning

Ivana Cechova and Matthew Rees

71

Strategies for Coordinating On-Line and Face-To-Face Components in a Blended Course for Interpreter Trainers

Barbara Class

78

iBuilding for Success? iBooks as Open Educational Resources in Built Environment Education

David Comiskey, Kenny McCartan and Peter Nicholl

86

Facilitation of Learning in Electronic Environments: Reconfiguring the Teacher’s Role

Faiza Derbel

94

Effect of e-Learning on Achievement and Interest in Basic General Mathematics Among College of Education Students in Nigeria

Foluke Eze

101

Self-Organization of e-Learning Systems as the Future Paradigm for Corporate Learning

Gert Faustmann

106

An Online Tool to Manage and Assess Collaborative Group Work

Alvaro Figueira and Helena Leal

112

Design 4 Pedagogy (D4P): Designing a Pedagogical Tool for Open and Distance Learning Activities

Olga Fragou and Achilles Kameas

121

The Affordances of 4G Mobile Networks Within the UK Higher Education Sector

Elaine Garcia, Martial Bugliolo, and Ibrahim Elbeltagi

131

An Integral Approach to Online Education: An Example

Jozef Hvorecky

139

i

Tasks for Teaching Scientific Approach Using the Black Box Method Martin Cápay and Martin Magdin Department of Informatics, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Slovakia [email protected], [email protected] Abstract: The Black Box is a substantial system with internal organization, structure and element behaviour, about which the observer has no information, but has the option to impact the whole system via its inputs and observe its reactions via its outputs. The observation of the black box is therefore behavioural. In the paper, we point out the possibility of alternative methods of teaching algorithms using the Black Box Method, which raise the curiosity of students and their desire to uncover the mystery. These boxes should be used as group activities at school or even as a solitary activity at home. Black box applications are also suitable as a part of e sources in blended learning methods. In the paper, we will present tasks that can be programmed and in which it is important to know the basic, often very simple, mathematical relations. The activities described in the paper can be used in mathematics lessons and extended on programming lessons. We strive to describe how to use black boxes in educational practice and, furthermore, how to present principles of reverse engineering. We teach them the basic skills of scientific approach: to analyze the current situation, form hypotheses and consequently verify the correctness of stated assumptions. We mostly presented the tasks on a variety of training and educational workshops. In our experience, applications functioning as black boxes were proved to be adequate recovery activities within longer lectures (university), as well as an example of playful learning activities. This concept can be used in teaching, or even in leisure activities, for the activation of students. The contribution of the Black Box Method lies in development of students curiosity, systematic approach and critical thinking while gaining new knowledge. Keywords: activating teaching methods, black box, scientific approach, algorithm, blended learning, modelling and simulation

1. Introduction Several years ago, we were fascinated by some interesting applications that students brought to the class called Creation of the educational software. They brought some miracle applications that could create an effect of reading users thoughts. Students knew how to create applications, they were able to present them and they also knew the steps for revealing the solution to the mystery, which was usually a number. However, they did not know the answer to our question: How (Why) does it work? Some of the solutions were very transparent. Solutions often enclose complex mathematical formulae. But the students were satisfied with their results without knowing what lies behind the applications. None of them wanted to think about the applications substance. They did not want to know why these applications were programmed this way. The development of algorithmic and problem thinking is very important not only for the school environment, but also for a large number of activities in real life. In some situations, we have to clearly describe this process in a numbered sequence using branching or repeating; formulate the abstract terms or create formulae. In other words, we deal with the concept of algorithm, problem analysis and algorithmic thinking. But if we want to describe real life problems using a computer program we must have the basic skills of scientific approach: to analyse, to formulate hypotheses and to verify. After this experience, we decided to use more magic applications during the educational process to increase student s interests for scientific approach. In previous work we try to present some constructivist black boxes tasks and solutions (Cápay and Magdin, 2011; Cápay and Magdin 2013) or tasks that could be used such as computer unplugged activities (Cápay 2013). In this paper, we briefly describe the black box model (BBM), using BBM in education and introduce the realization of the BBM in Computer Science and Mathematics Teaching. We present the tasks that can be programmed and can be used as e study applications.

2. Analysis of the complex model functionality using the black box method The black box method is based on analysis of the system behaviour without knowing its internal structure. It is based on analysis of the complex closed model functionality. The desired outcome is the correlation between inputs and outputs.. The Black Box Method (BBM) is considered to be a constructivist teaching method. Constructivist methods are procedures leading the teaching towards achieving the educational goals, in particular based on the authors

Martin Cápay and Martin Magdin own work (Guni et al. 2009). Constructivist teaching methods do not usually require raising financial resources, ensuring special teaching aids, technologies, nor creating or building special classrooms (Sirotová, 2010). We can activate a student s activity even by a simple change of the concept of lessons, for example, by regular tasks, the solving of which will be rewarded by extra points for the final evaluation (Vilonen, Zizzing and Krause, 2008). In general, the Black Box is a substantial system (object, process, and phenomenon) with internal organization, structure and element behaviour, about which the observer has no information, but has the option of impacting the whole system via its inputs and registers its reactions via its outputs. The observation of the black box is therefore behavioural. The observer affects the black box via its input and gets information from its output (0). In this way, the observer and the black box create a system with feedback. In our daily lives we are confronted with many systems whose internal mechanisms are not fully open for inspection, and which must be treated by the methods appropriate for the Black Box (Ashby 1956).

Figure 1: Scheme of the method of black box principle Theoretical aspects and the application of the black box and the way of its using in different scientific fields such as pedagogy, software engineering, cybernetics, finance, physics, mathematics, electrical engineering, computer science or even in philosophy and psychology was already described (Amato, 2010; Brunsell, 2010; Lederman and Abd El Khalick, 1998; Onderová, 2009). Tang (2013) describes significant advantages in solving engineering optimization problems with black box functions. The problem of the Black Box in electrical engineering was first used in 1956 by Ashby. According to him, the engineer is given a sealed box that has a terminal for inputs, to which he may bring any voltages, shocks, or other disturbances he pleases, and terminals for output, from which he may observe what he can. He is to deduce what he can of its contents (Ashby 1956). Apresian (1960) pointed out that the use of the BBM started in electrical engineering, specifically in the area of modelling. He formulated four steps for the BBM: definition of facts that need to be clarified, formulating hypotheses suitable for facts clarification, realization of hypotheses into the models, that not only clarify the outgoing facts, but also predict the new one, experimental proof of the model. MS Excel could also be used as a kind of Black Box. Its calculations can be seen as program outputs of hidden programs. By drawing the curtain away from the hidden processes, one can illustrate concepts and properties that are typical for all programs (Lovászová and Hvorecký, 2005). Gal Ezer (2004) uses the new approach and advocates integrating three part questions which ask not only to identify the problem that a given algorithm solves and to analyze its complexity, but also to design a new algorithm that performs the same task.

2.1 The black box in education Applying the BBM, we teach the students to systematically examine the things around them and think about the technologies they use. Learning using the black box is a natural way of learning. We learn to open the door using the handle, we learn to lock the door, to turn on the CD player, etc. The learning is often conducted only on the basis of experiment, with no knowledge of the inner structure of the object. How should we execute the BBM in educational practice? At the beginning of the experiment, there are no assumptions about the black box operation. The student works with an application that is acting mysteriously,

Martin Cápay and Martin Magdin for example, reading their mind. Of course, the student immediately thinks that the whole program works on the basis of some kind of hidden principle (algorithm) they just do not know what it is yet, so the application is a mystery to the user. Only a mechanism (instructions) is available, showing ways of interacting with the box (inputs). In this way, the students can experiment with the application, explore its reactions (outputs) to different inputs, trying to solve this mystery . The students aim is to observe the interaction between inputs and outputs and to make a protocol about their observations, which will be the basis for the formulation of the algorithm operating the box (Cápay et. al. 2011b). While executing this type of task, it is necessary to consider the student s ability to solve the task using the knowledge s/he already has or can easily deduce. The most important moment of using methods for activating students is so called aha effect . It is the moment when student begins to understand the heart of the matter (Cápay, 2013). The contribution of the BBM lies in development of students curiosity, systematic approach and critical thinking while gaining new knowledge (Guni et al., 2009). We teach them the basic skills of scientific approach: to analyze the current situation, formulate hypotheses, verify the correctness of stated assumptions. Experience lets us confirm there is a difference between so called ordinary observer and a research type (Ashby 1956). The ordinary observer asks the question What is in this Box? , s/he wants to know the answer, and does not care about the black box anymore. The researcher asks questions such as How should I proceed when I am facing a Black Box? , What methods should be used if the Box is to be investigated efficiently? We believe that the BBM is a suitable method to determine the student s learning style. We presume that a student preferring an active and intuitive learning style will get better results in experimenting (Felder Silverman Index of Learning Styles, Hawk and Shah, 2007) than her/his classmates. This can be grounds for another research.

3. Realization of the BBM in computer science and mathematics teaching There are a number of tasks that, on the one hand, meet the condition of mathematical essence of the problem solution (Cápay and Magdin, 2011); on the other hand, they are not suitable for the black box method, as they do not include programming the relevant algorithm and they cannot be used in an electronic environment. It is therefore not possible to create a functional application that would act as the black box for the experimentation. We suggest the following types of tasks. The presented principles can be transformed into computer applications. We search for such tasks that motivate and mobilize students to seek the essence of the task first impression of which is mystique . Tangible black boxes can be then substituted by electronic model. In all of the mentioned examples, it is then presumed that the student (experimenter) has access to functional magical applications. We mostly presented the tasks on a variety of trainings and educational workshops (Cápay et al. 2011a, 2011b) where they provoked very intensive reactions and even activated the teachers themselves (Figure 2). They considered the tasks to be interesting; many were surprised by the simplicity of the algorithm behind the magic. In our experience, applications functioning as black boxes were proved to be adequate recovery activities within longer lectures (university), as well as an example of playful learning activities.

Figure 2: Presentation of Black Boxes in educational workshops and trainings (in 2013, in 2012, in 2011)

Martin Cápay and Martin Magdin

3.1 Logical circuit realization Models are often used in the educational process, even at the time we do not perceive them. Model is a system developed with the purpose to visualize selected aspects of the real object. It is a simplification of the real object that sustains the sufficient accuracy of modelled aspects. Students at the Department of Computer Science FNC CPU in Nitra in the subject Computer Architecture are conversant with basic properties of electrical and electronic components. They deal with logical systems (combinational and sequence logical circuits) simulated by MultiSim and LogicSim. At the beginning of the semester, the group consists of students with different grammar education. We try to use several methods to get all students to the same knowledge level. The BBM allows students to develop motoric and intellectual skills. The BBM is used in final testing. Students have to figure out the functionality of the hidden system created in LogicSim (03). They come out from previous experience with the logical circuits scheme realized in Multisim or from the real component connection. The aim of the students is to estimate the internal system function. Manipulation with these black boxes leads students to better comprehension of AND/OR circuit functioning (04).

Figure 3: The black box implemented in LogicSim

Figure 4: Behind the the black box and/or circuites

3.2 Linear dependence The task is then to find an algorithm (formula) which will generate each particular sequence. The task could read: Complete the following sequence with a number which will suit a hidden rule, according to which the sequence is generated" (05). We used an XLS document with hidden cells, secured cells and simple macro formulae.

Figure 5: Linear dependence tasks complete the sequence

Martin Cápay and Martin Magdin The second type of tasks is a modification of the first one. It focuses on a search for linear dependence based on manipulation with the application. It is necessary to define the formula or verbally render the rule, based on which the input transforms itself into output (0). An XLS document with hidden cells, secured cells and simple macro formula was used again.

Figure 6: Linear dependence tasks (sum of ciphers) [right]

define the formula expected output: 20 (x 1) * 5 [left]; x

4 [middle]; x

3.3 Text strings The black box designed to exercise the work with text strings is suitable for exercising the ciphering algorithms. Ciphering is a transformation of information from one form into another with the purpose of hiding the real content of the information from the eyes of unwanted persons. For exercises related to ciphering to be used in the black box education, it is necessary that the problems can be solved using the knowledge gained in a given moment. If we needed complex keys or mechanical tools to solve the problem, the use of this method for educational and algorithmic purposes would be significantly narrowed. We consider the tasks aimed at text ciphering using the following types of ciphers to be suitable exercises, particularly for computer science (07): Cheaters the principle lies in inserting of a selected string to a chosen place in the original text. For example, each vowel will be succeeded by the consonant p and the vowel itself, i will be ciphered as ipi , o as opo etc. Transposition ciphers the principle lies in preserving the former identity of a character, the only occurring change is the one of its position. For example, each pair of letters will be changed its position. Substitution ciphers the principle lies in preserving of the position of a character and in the change of its identity. For example, each letter of the input string will be shifted in alphabet for three (or other) positions to right.

Figure 7: Ciphering algorithms cheater, transposition, substitution

3.4 Algebraic modifications The algorithm works on a principle of successive instructions for the user from the black box. It begins with the instruction think of a number followed by a sequence of instructions containing mathematical operations such as add , subtract In the end, the application, despite not knowing the number you have thought of, guesses the result the user got following the instructions on mathematical operations. The first instruction 1. Think of any number you want. 2. Add 5 to it. 3. Multiply the sum by 2. 4. Subtract 4.

1. 2. 3. 4.

The second instruction Think of a number from 1 to 10. Add 8 to it and multiply the sum by 2. Subtract 16 and multiply by 2 again. Divide by 4 and add 8.

Martin Cápay and Martin Magdin 5.

Divide it by 2.

5.

6. 7.

Subtract the original number from the result. The result is 3.

6. 7.

Subtract the original number from the result and multiply by 2. Add 9. The result is 25.

The algorithm is designed so that the operations of addition, subtraction, multiplication and division are served to the user strategically according to a set key (formula 1, formula 2):

The original value will be eliminated in one of the steps, making it irrelevant to the total result of the expression. Thus, the algorithm never depends on the chosen number; instead it makes the user think of the number chosen by the algorithm. It would be appropriate to parameterize this type of task so that the user will always get a different, random result while experimenting with the black box.

4. Conclusions Education and possible further studies have become a rather demanding investment. Looking for different techniques and ways to make learning process more attractive with minimize financial costs is important. It could be done by combination of traditional teaching and ICT resources (Balogh, Tur áni and Burianová, 2010). The Black Box methods could be one of the solutions. This concept can be used in teaching traditionally or online, or even in leisure activities, for the activation of students. The activities described in the paper can be extended on the programming lessons. After experimenting and disclosure of the essence of the mysteries, we can ask the students to create an application with the same functionality. Consequently, we can discuss the possibilities of the application s enhancement or modification with the students, for example by generalizing the idea explaining the mystery or using analogy. The students can make up their own magic trick and present it to their classmates. We think that using the black box methods is good for developing logical thinking and motivating the students to find the hidden solution. As we mentioned above, we mostly presented the tasks on a variety of educational workshops, but we want to convey that they could be used also a part of e courses.

Acknowledgements This publication is supported thanks to the Fund for supporting the Centres of Research and Development with internationally comparable quality of operations, Faculty of Natural Sciences, CPU Nitra, Slovakia and thanks to the financial support of the project 015UKF 4/2013 Modern computer science new methods .

References Amato, J. (2010) Using the Black Box Approach to Enliven Introductory Physics Labs , Forum on Education, American Physical Society, 2010, pp 30 31. Apresian, J. D. (1960). Leksi eskaja semantika. (Lexical Semantics). Moskva, Nauka. Ashby, W. R. (1956). An introduction to Cybernetics. William Clowes and Sons, Limited. London and Beccles. Balogh, Z., Tur áni, M., and Burianová, M. (2010) Modelling web based educational activities within the combined forms of education with the support of applied informatics with an e learning support. Efficiency and Responsibility in Education ERIE 2010. Praha Brunsell, E. (2010) How to teach Students to think like Scientists http://www.edutopia.org/blog/how to teach students to think like scientists?page=1 Cápay M. (2013) Developing of Algorithms Thinking Using Activating Methods. Efficiency and Resposibility in Education ERIE 2013. Praha Cápay, M. and Magdin, M. (2011). Hlavolamy, kódy a ifry podporujúce algoritmické myslenie (Brain teasers, codes and ciphres supported algorithmic thinking) . Alternativní metody výuky (Alternative teaching methods). Praha. Cápay M. and Magdin M. (2013). Alternative Methods of Teaching Algorithms. Procedia Social and Behavioral Sciences. Vol. 83 ( 2013 ), pp. 431 436. Cápay, M., Kapusta J., Magdin, M., Mesáro ová, M., and vec, P. (2011a). Scientific Fair : science you can see, hear and experience. Interactive Collaborative Learning 2011 (ICL 2011). pp 487 491.

Martin Cápay and Martin Magdin Cápay M., Kapusta J., Magdin M., Mesáro ová M., vec P. and Valovi ová . (2011b). Popularizing Natural Sciences by Means of Scientific Fair. International Journal of Emerging Technologies in Learning (iJET). Vol. 6, no. 4 (2011), pp. 12 17. Gal Ezer, Judith. (2004). Teaching Algorithm Efficiency at CS1 Level: A Different Approach. Computer Science Education . Vol.14(3), pp 235 248. Guni , J., Súdolská M., and najder, . (2009). Aktivizujúce metódy vo výu be kolskej informatiky (Activating Teaching Methods in School Informatics). Bratislava Hawk Thomas F., Shah Amit J. (2007). Using Learning Style Instruments to Enhance Student Learning . Decision Sciences Journal of Innovative Educatio. Vol. 5, no. 1, pp 1 19. Lederman, N.G. and Abd El Khalick, F. (1998). Avoiding de natured science: Activities that promote understandings of the nature of science. , The nature of science in science education: Rationales and strategies (pp. 83 126). Dordrecht, The Netherlands: Kluwer Academic. Lovászová, G. and Hvorecký, M. (2005). Using spreadsheet calculations to demonstrate concepts of programming. International Journal of Continuing Engineering Education and Life Long Learning. Volume 15, Issue 3 6, 2005, pp 162 184 Onderová, . (2009). Physics : black box? In:Science in School. ISSN 1818 0353. iss. 12 (2009), p. 40 43, dostupné aj v sloven ine online: http://www.scienceinschool.org/2009/issue12/blackbox/ slovak Sirotková M. (2010). Activating Methods: Tools To Increase Quality And Effectiveness Of Tertiary Education. The future of Education. Florence. Italy. Vilonen K., Zizzing E., and Krause O. (2008). Use of activating teachnig methods in an introductory course of chemical processes. SEFI annual conference in Aalborg. Tang Y,, Chen J. and Wei J.. 2013. surrogate based particle swarm optimization algorithm for solving optimization problems with expensive black box functions. Engineering Optimization, 45(5), 557 576.