trends of elearning: learning - knowledge - development

TRENDS OF ELEARNING: LEARNING - KNOWLEDGE - DEVELOPMENT Marin VLADA ∗ Adrian ADASCALITEI ∗∗ Radu JUGUREANU ∗ ∗∗∗

Abstract: This paper presents several important topics that show the importance of learning and knowledge in the development of human society. Knowledge depends upon the learning process. It shows that in the problem-solving processes require demonstrative thinking, a algorithms thinking. It presents considerations on the future of e-learning.. Keywords: e-Learning, knowledge, problem solving, knowledge representation, creativity, development

I.

INTRODUCTION

Computer Science (Informatics) is characterized by the most spectacular evolutions of the impact on human activity. Computer (Computer System) includes technologies of which man has never dreamt. Although at the beginning the use of computer was regarded with reservation, nowadays most of the people are convinced by the performance and utility of computer in all activities. Today, starting from primary school children find out about the impact of computer in their lives. Because of these reasons, the educational systems of many countries are conceived to implement developing strategies oriented to computer utilization for both initialization and continuous learning process 1 . At the beginning of the 21st century it may be said that information and knowledge are found on the base of scientific, technological, economical, social, cultural processes/events. The economist Roger E. Bohn shows that now it is important to understand technological knowledge, and more specific, the way of producing goods and services. Knowledge depends upon the learning process. It is worth reminding Bohn's concept "Learning is evolution of knowledge over time" 2 . „Today’s pupils took part actively in transforming the IT labs in classrooms, redefining IT as a support for teaching and the computer as a support for training. We are determine to involve the pupils more and more in developing their own knowledge as well as in the process of creating educational resources meant for future generations” 3 [Vision 2020 – How Pupils See the Future of Education].

II.

SEI IT EDUCATIONAL SYSTEM – ROMANIAN PROJECT

The IT Based Educational System (SEI) is a complex program initiated by the Ministry of Education, Research and Innovation in 2001, aiming to offer ITC support for the Romanian education

1

M. Vlada and Al. Tugui, The four waves of information technologies, ICVL 2006 R. E. Bohn, Measuring and Managing Technological Knowledge, 1998 3 R. Jugureanu, The 6th edition of the National Competition for Educational Software Cupa SIVECO 2008 2

system 4 . The Program is implemented in partnership by the state administration (RMER) and the private sector. The main companies involved in SEI implementation are the Romanian company SIVECO Romania SA, HP and IBM. SEI is aiming to provide all schools in Romania with complete IT solutions for use in the teaching/learning process 5 . Also, the SEI program promotes IT&C in education through specific projects designed both for administrative and educational purposes. The SEI Program offers new tools for use in schools, thus increasing the quality of the education process. It offers a substitute for expensive or dangerous instruments and experiments by means of virtual counterparts. Within SEI. Program, the local, regional and country administration is provided with managerial and administrative support. The main components of the solution are: • Hardware (IT laboratories) • Learning, Content Management Solution (the AEL software system) • Educational software and electronic educational content • Teacher training • Internet connectivity. AeL – Teaching / Learning and Management System for Multimedia Educational Content. AeL is an integrated Learning and Content Management System developed by SIVECO aimed to support professors/tutors, students, content editors, administrative staff and other stakeholders in the learning process. AEL is qualified of management and delivery of various content types such as interactive multimedia, tutorials, exercises, simulations, educational games etc. Its powerful knowledge base, which acts as a content repository and management solution, adaptive, configurable and searchable, allows first-time users to easily: • create content (built-in HTML editor, mathematical formulae editor, test editors and wizards, glossaries/dictionaries editor) • import/export content from files, archives/folders of resources, standard packaging formats like SCORM and IMS • adapt or modify content • derive their own courses from common content components. AEL as a multi-tier system - with a thin, web client connected to a Java based web and application server. It employs Enterprise Java Beans, Jdbc, Java servlets, jsp-s, Java applets, and makes extensive use of XML. Considering the need for content interoperability; the content packaging formats are based on XML and AEL implements support for standard content packaging and interoperability formats like MathML, SCORM 6 and IMS.

Fig. 1. Sei educational portal (Siveco) 4

SEI educational portal, http://portal.edu.ro (romanian project) CNIV and ICVL Projects, www.cniv.ro (romanian project), www.icvl.eu (international project) 6 ADL, http://www.adlnet.gov/scorm/index.aspx 5

The European IT Excellence 2008 awards promoted by the prestigious publication – IT Europa, rewards annually the most efficient software solutions designed for commercial and governmental organizations (Fig. 1). The quality of the implementation as well as the impact of the SEI project (The IT-based Educational System) were the main reasons for which SIVECO Romania managed be successful in the European IT Excellence Awards 2008 Gala, receiving a new and prestigious European recognition 7 . “The eLearning solution provided by SIVECO Romania for the country’s Ministry of Education Research and Youth is an excellent example of how to deploy a multimedia based content management system tailored for a dynamic educational environment”[ European IT Excellence Awards 2008] 8 SIVECO Romania 9 and SANAKO launched the Virtual Lab for science experiments. BETT 10 2009 , the largest educational technology exhibition, took place between the 14th and the 17th of January in London. 650 companies presented innovative solutions for the 21st century education, and the organizers estimated that more than 30,000 visitors from all around the world visited Olympia Hall (Fig. 2). As every year, at BETT are hosted revolutionary education products launches. The new products encourage the use of modern technologies for developing the education systems to better face the 21st century challenges (Fig. 3). SIVECO Romania and SANAKO Corporation launched SANAKO Study Science Lab.

Fig.2. The Virtual Lab for science experiments – BETT 2009, London – Olympia Hall

Fig. 3. CNIV and ICVL Projects sponsored by Siveco Romania 7

Press Releases, www.siveco.ro John Chapman, awards organizer and Editorial Director of IT Europa 9 Press Releases, www.siveco.ro 10 BETT 2009, www.bettshow.com 8

III.

PROBLEM SOLVING – CORRECT THINKING

At present the scientific and technical development, solving problems in a different field (math, science, physics, chemistry etc.) is a creative activity, by building a reasoning, generation, describing the following activities 11 : • demonstration process (deduction and reasoning) to show the existence of a solution or several solutions and / or to determine the exact effective solutions; • computational process (algorithm) to codify a demonstration, a method or technique to solve in order to determine (possibly approximate) exact solutions. In the problem-solving processes require demonstative thinking, a algorithms thinking. From the methodological point of view, we need to recast usual problems explicitly and properly resolve their mathematical. If the computer should use to develop algorithmic methods. In both cases you must know the limits of thinking demonstration 12 . You should also know the limits of performance computing and algorithmic thinking. Mathematics in primary grades and the average result of obtaining competent in solving problems. Logic, reasoning, knowledge organization and processing are the basic elements for a experience in solving problems. This will be the basis of a scientific education for a future specialist or expert. Today, when the computer is used a lot in various activities intellect is needed to increase teachers' role in the formation of correct thinking must head all students. 3.1 Demonstration process – human intelligence Sciences are virtual representations of knowledge 13 . Every science is based on the theories, theorems (laws) and hypotheses that have been identified, studied and demonstrated by the strengthening, development and evolution in time of sciences. For example, computer science (informatics) is a science that uses new research and results of mathematics, cybernetics, microelectronics, biology, chemistry, psychology. Today research are to build molecular computer 14 . In Romania, researchs are coordinated by acad. Gheorghe Paun. (G. Paun and T. Yokomori. Simulating H systems by P systems. Journal of Universal Computer Science, Vol.6, No.1, 2000, pp.178-193). Problem solving and research is based both on the demonstration process, and the algorithmic processes. A specialist or expert gains experience in the activities of problem solving and research only through learning and training. Artificial intelligence (AI) provides new methods and modern techniques for solving problems. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication. Artificial intelligence 15 (The Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory -CSAIL conducts research in all areas of computer science and AI, such as robotics, systems, theory, biology, machine learning, speech recognition, vision and graphics) is the intelligence of machines and the branch of computer science which aims to create it. Is there an essential difference between human intelligence and artificial intelligence? It is known that man can solve a problem in three ways (methods outlined in Artificial Intelligence): • consecution / sequentially before method - is based on the hypothesis / premises (input data) to reach conclusion (output data); • consecution / sequentially back method - is based on conclusion (output data) to reach the hypothesis/ premises (input data); • mixed method - to start simultaneously from the hypothesis/ premises and the conclusion to arrive at a meeting in the browsing. This presentation can be understood if, for example by making analogy solving geometry requiring delineation hypothesis and conclusion, then use only those theorems / sentences / properties from the hypothesis by applying sequentially to obtain the conclusion required (Fig. 4). Obviously, the 11

M. Vlada, Algorithmic thinking, http://ebooks.unibuc.ro/informatica/eureka/index.htm, 2003 Adăscăliţei, Adrian, Instruire asistată de calculator–Didactică Informatică, Editura Polirom, Iaşi, 2007 13 M. Vlada, “From CNIV2003 to CNIV2008: Learning - Knowledge - Development”, CNIV 2008-Constanta 14 Masami Hagiya, http://nicosia.is.s.u-tokyo.ac.jp/MCP/ 15 Learning and Intelligent Systems, ttp://www.csail.mit.edu/ 12

experience gained in solving problems, it can be affirmed that the selection theorems and their application can be achieved only through "mastery" domain-level specialist or expert. It can be assumed that the representation of knowledge in a field of science is through theorems, properties, laws (which are scientific truths). Therefore, a theorem, a sentence, a law and a problem can be defined and set out the following form and description: T: I Æ C, where I is hypothesis and C is conclusion. This representation can be read as: if I is true then C is true. This representation is encountered as instruction (eg, decision) in procedural programming languages (imperative), but also in declarative programming languages (logic, eg Prolog - language of artificial intelligence; Prolog is a logic programming language) in the form of Horn clauses 16 (Clocksin, W. F. and Mellish, C. S. Programming in Prolog Using the ISO Standard. New York: Springer-Verlag, 1984). His dual declarative/procedural interpretation later became formalised in the Prolog notation H :- B1, …, Bn, where H, B1, …, Bn are all atomic predicate logic formulae(If B1 and … and Bn then H). Suppose the problem solved in a domain. Therefore, a problem can be defined and set out the following form and description: P: I Æ C, where I is hypothesis and C is conclusion. Solving problem can be represented by formal proof, mathematical proof or proof theory. A demonstration be represented by a sequence of rules / theorems (One of the main uses of a propositional calculus, when interpreted for logical applications, is to determine relations of logical equivalence between propositional formulæ. These relationships are determined by means of the available transformation rules, sequences of which are called derivations or proofs 17 .). Solving process is identifying what theorems can be applied so as to finally reach conclusion. Theorems T1, …, Tn are determined by assumptions I1, …, In the conclusions and C1, …, Cn. They were selected so as to make identification: I I1, C1 I 2 , … , Cn C.

Fig. 4. Consecution before method Problem solving forms part of thinking. Inventing is a special kind of creative problem solving in which the created solution qualifies as an invention because it is a useful new object, substance, process, software, or other kind of marketable entity. 3.2 Computational process - human intelligence Algorithmic method can be a method to solve independent or an experiment to justify the results of the demonstration process (mathematical method). Algorithmic method should replace results about the theoretical computational (demonstration process) efficient methods taking into account the limits of algorithmic processes and performance computing. It is true that mathematical methods can sometimes lead to simplification of algorithmic methods and vice versa. Students participating in informatics olympiads schools (The International Olympiad in Informatics 18 - IOI is an annual informatics competition for secondary school students) have sometimes found that a deeper analysis of the mathematical approach can lead to obtaining an efficient algorithmic methods. Students mathematicians may find the same thing to a deeper analysis of algorithmic approach 19 . The complexity of problems that require description of several complex processes of calculation led to the concept of algorithm used in solving problems. Many natural processes, many 16

Wolfram Math, http://mathworld.wolfram.com/HornClause.html Stefan Bilaniuk, Deduction Theorem, http://us.metamath.org/mpegif/mmdeduction.html#dedvsth 18 IOI 2009, http://www.ioinformatics.org/index.shtml , http://www.mii.lt/olympiads_in_informatics/(Journal) 19 E. Cherchez, Romanian National Olympiads in Informatics and Training, http://www.mii.lt/ , 2008 17

human activities, can be described in an algorithmic definition of the information and action clear and precise, eliminating ambiguities in the interpretation and operation. Algorithm process is a fundamental requirement in solving any problems with the computer. Experience has shown that not every problem can be solved by describing an algorithm to solve. So to decide limited class of problems (one problem is deciding if there is an algorithm for solving them) class issues non-deciding (P is the class of decision problems solvable in deterministically in polynomial time; NP is the class of decision problems solvable nondeterministically in polynomial time 20 ). An algorithm implementing various methods and techniques to solve that were discovered or completed within a certain time in the evolution of the scientific domain. There are algorithms that include methods developed before the computer. There are also areas in which problems need solving approaches. An example is “The four color problem 21 ” that was resolved in 1977 simply by using computer and using a new method (Backtracking). The fact that there are alternative ways of executing a logic program has been characterised by the equation: Algorithm = Logic + Control (R. Kowalski 22 1979) where "Logic" represents a logic program and "Control" represents different theorem-proving strategies (Logic for Problem Solving. Artificial Intelligence Series North Holland, 1979). Today is more often invoked representation problems using OOP concepts (Object Oriented Programming). The concept of object (M. Minsky, The Society of Mind, Touchstone Books, New York, 1986) has an important role in science knowledge and education. In traditional programming, control flow is primary and data are secondary. In object oriented programming, it is the opposite. Object oriented features are extremely useful in providing encapsulation and protection mechanisms and promoting modularity and code reuse. The theory of object oriented programming is currently a very active area of research, with at least three major conferences devoting significant portions of their time to the topic, some exclusively. The semantics of objects, especially their interactions with types, are not fully understood. An entity object model from the real world or virtual. In the problem-solving should identify / define the objects in the problems from different fields: scientific, economic, social. Identifying objects is equivalent to the concepts and entities representing physical forms / charts, facts, events, processes, states. An object is characterized by a unique identification, behavior (dynamic characteristics) and status (static feature). Essentially, solving a problem is expressed by a codification of the universe problem and deduction / reasoning for the demonstration (poof). Today, the specialists working in a certain field, face different complex problems, many of these requiring the use of computer and software products. The complexity of activities, competitions of all kinds, efficiency require the use of the best software and hardware products. The explosion of tools and methods offered by information and communication technologies (IT&C) can be easily seen, by computing systems, by peripherical equipments with different functions. There are more and more research, development and innovation programs and results do not delay to appear. At the same time, continuous learning, the use of new knowledge in the activity field must be major goals of every specialist. Synthetic following figure show the relationship of these issues.

Fig. 5. Solving problem using computer 20

David Johnson, Theory as the Scientific Foundation of Computing, http://www.research.att.com/~dsj Robin Thomas, http://www.math.gatech.edu/~thomas/FC/fourcolor.htm 22 Robert Kowalski, http://www.doc.ic.ac.uk/~rak/, http://en.wikipedia.org/wiki/Robert_Kowalski 21

IV.

LEARNING, KNOWLEDGE AND DEVELOPMENT

The research and development, innovation and improvement, foundation work and new concepts for the implementation and development of modern technology on the use of computers led to define the following pseudo-equation: • AlGORITHM = LOGIC + CONTROL ( R. Kowalski 1979 ) • PROGRAM = ALGORITHM + DATA STRUCTURES ( N. Wirth 1976 ) • EXPERT SYSTEM = KNOWLEDGE + META-INTERPRETER (Sterling 23 1984) Propose the following 24 : • MODELING = KNOWLEDGE + REPRESENTATION • LANGUAGES = PROCESSING + INTERPRETATION (CNIV – National Conference on Virtual Learning, www.cniv.ro, Romania) Today, the performance of IT developer is determined by experience and expertise gained in conducting the two stages (ANALYSIS, PROGRAMMING): • •

thinking object stage (ANALYSIS / Projection) - method of analysis and description of the problem by defining the correct objects, the types of objects, relationships between objects and specific operators (UAP development, stage design and analysis-design); algorithmic thinking stage (PROGRAMMING / execution) - the choice and proper application methods of solving the exact specification of the operators of processing objects, the correct representation of algorithmic strategies, codified representation of objects and processing according to a programming language (and algorithm development program; stage programming - coding implementation and enforcement).

At the beginning of the 21st century it may be said that information and knowledge are found on the base of scientific, technological, economical, social, cultural processes/events. The economist Roger E. Bohn shows that now it is important to understand technological knowledge, and more specific, the way of producing goods and services. Knowledge depends upon the learning process. Until now are valid as follows: “Information is power”, “Knowledge is power” and “Learning is power”. Today no country in the world can not ignore investment in education, knowledge and development. Including a great variety of information processing and a great utility of these processing in all activity fields. However, at each development level of the human society there was a foundation on information. Information is the primary form of getting knowledge. Among the significant examples, we enumerate the following: ABAC (Abacus) (3000 BC), paper (50 BC); printing press (1452); newspaper (1700); telegraph (1837); photography (1839); telephone (1876); electricity (1882); tabulator (1890); film (1891); radio - television (1920-1936); robot (1921); transistor (1947); graphic display (1953); integrated circuit (1959); microprocessor (1971), Web Technology and Internet network (1991). All these contributed to a better utilization of information in society and to an increase of human welfare and knowledge. In other words, it can be said that the global information society is the normal human society of all times with a informational modernism stamp due to the informational and knowledge avalanche. In the

23 24

Expert System = Knowledge + Meta-Interpreter, Tech. report CS84-17, Weizmann Institute of Science, 1984 M. Vlada, National Conference on Virtual Learning, CNIV 2005, Bucharest, Romania

period 2012-2030 it is desired to pass from an information society to a knowledge society. European programmes (FP6, FP7) are conceived to fulfil this goal. LEARNINGLEARNING-DEVELOPMENTDEVELOPMENT-KNOWLEDGE PhysicalEnvironment Environment Physical

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Thistreasure treasurecan can This NOTstore store NOT

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CNIV 2008 | 31 oct.oct.-2 nov.

Fig. 6. Learning - Knowledge - Development 25 The waves of the information technology 26 The results and performances in the fields of computer science, telecommunication and information technology have always been spectacular. Today, many computer types are meant to fulfil human dreams about a partial or integral cybernetic world and a super library of information. In other words, at the base of the tomorrow society will be information, knowledge and communications. A passing schedule towards the global information society is given by J.A. O’Brien 27 , who considers that reaching this stage requires passing through 4 stages (Fig. 7): 1. 2. 3. 4.

the stage of the computerized enterprises, for the period 1970-2012 – the first wave; the stage of the networked knowledge workers, which started in 1980 – the second wave; the stage of the global internetworked society which started in 1991– the third wave; the stage of the global information society which will start after 2010– the forth wave.

Fig. 7. “The waves of the Information Technologies” and “Learning - Knowledge – Development” Network 28 (Romania) 25

M. Vlada, National Conference on Virtual Learning, CNIV 2008, Bucharest, Romania Boar, B. H. (2001): The Art of Strategic Planning for Information Technologies, 2nd edition, John Wiley & Sons, Inc., New York. 27 O’Brien, J. A. (1999): Management Information Systems: Managing Information Technology in the Internetworked Entreprise 28 M. Vlada, http://virtuallearning.ning.com 26

V.

THE FUTURE OF ELEARNING: LEARNING

“Any sufficiently advanced technology is indistinguishable from magic.” Arthur C. Clarke “Human society development is achieved by knowledge and learning.” M. Vlada and Al. Tugui E-Learning is an important consideration in education for several reasons: Implimented correctly, it can reduce some of the costs associated with education; It allows schools to educate people they could not previously (e.g. people that work for a living, people geographically dispersed, etc..); Many students communicate better in a web based environment than in the traditional classroom. Studies have shown that students who would not raise a hand in class will be very active in posting to discussion boards for example. E-Learning is a challenge for educational institutions because the technology involved can be difficult to manage and use. A lot of training or practice is required to get proficient in e-Learning solutions. Also, self-paced WBT courses are very time consuming to create and really only make since when there is a large learning audience (e.g. at a large corporation). Tony Karrer 29 has created a Web site called eLearning Learning that collects postings from a variety of eLearning professionals' blogs and organizes them in one place. Learning Developments is now featured there. Tony describes his site as follows: "eLearning Learning is a community that tries to collect and organize the best information on the web that will help you learn and stay current on eLearning. eLearning Learning can be a good place to search for eLearning specific information that has been posted on many of the members' blogs”. In this world, probably the major trend that we’ve seen is a demand for faster learning in the context of work. We’ve also seen the slow smushing together of Online Reference, Online Job Aids, small eLearning pieces, Rapid eLearning and Blended Learning. Concept 30 of eLearning 2.0 starts with the trend towards: Small pieces of content; Delivered closer to time / place of work; Likely delivered in pieces over time as part of a larger program. (http://elearningtech.blogspot.com/2006/03/personal-learning-for-learning_20.html) The Future of Networking in Higher Education “Computers and networks have changed how research is conducted in many academic disciplines. New and emerging disciplines like computational chemistry, computational biology, bioinformatics, atmospheric informatics, and others bear witness to the revolution in scholarship that is under way.” ( Richard N. Katz 31 , http://net.educause.edu/ir/library/pdf/ERM0547.pdf) The Era of Data-Intensive Scholarship Professor Larry Smarr 32 describes the future as an "era of data-intensive science". At the November 2004 ECAR Symposium, Smarr described some of the key layers and elements that will constitute this era and the place of the network amidst this complex mix of technologies, academic disciplines, and human behaviours. In November 2004, the U.S. Congress approved a bill that increases funding for supercomputing initiatives in the United States and extends greater access to such systems to academic researchers. The era of data-intensive science ahead will not just be defined by or confined to supercomputing and scientific uses of data. Today’s young social science investigators are exploring big econometrics, big sociology, and even big history, for example. Choreographers are using visualization and simulation techniques to model and teach dance, orchestral performers conduct master classes across great distances, and literature scholars are using algorithms to conduct content analyses of texts long believed to have been "mined out." Toward Pervasive and Personalized Intelligence and Communications If the networking community’s battle cry of the 1980s was "information anytime, anywhere," and if that cry evolved in the 1990s into "what you want, when you need it," then perhaps networking’s 29

Tony Karrer, http://www.elearninglearning.com Tony Karrer ,http://elearningtech.blogspot.com/2007/03/elearning-trends.html 31 Richard N. Katz, EDUCAUSE Review, vol. 40, no. 4 (July/August 2005): 62–75 32 Larry Smarr, http://www.jacobsschool.ucsd.edu/~lsmarr/ 30

driving vision in the future will be "all you can imagine, all the time." This vision of the cyberinfrastructure suggests called the "death of distance" and the blurring of the lines between real and virtual in the context of learning and scholarship. In the coming years, the lines that distinguish the real from the virtual will indeed grow fainter, driven by four key trends: logical connectivity, smart and talkative devices, convergence, and personalized on-demand and reliable services. (Richard N. Katz, EDUCAUSE Review).

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Ph.D. Associate Professor, University of Bucharest, Romania Ph.D. Associate Professor, “Gh. Asachi” Tehnical University of Iasi, Romania ∗∗∗ Professor, eContent Manager, SIVECO Romania, Bucharest ∗∗