Ubiquitous Learning: Teaching Modeling and Simulation

with Technology

Dietmar P.F. Möller

Clausthal University of Technology, Institute of Applied Stochastics and Operations Research, Erzstr. 1, D-38670 Clausthal-Zellerfeld, Germany

dietmar.moeller@tu-clausthal.de Roland Haas

International Institute of Information Technology Bangalore, India reh@iiitb.ac.in

Hamid Vakilzadian University of Nebraska-Lincoln, Department of Electrical Engineering, USA

hvakilzadian@unl.edu

Keywords: Blended learning, e-learning, internet of things, lifelong learning, mobile learning, modeling and simulation, ubiquitous learning, remote access.

Abstract Education has undergone major changes in recent years in conjunction with the development of computers and information and communication technology (ICT) networks. This has enhanced access to global information and communication systems. Therefore, the number of resources available to today’s students at all levels of education has been enhanced. In its early form, computers and ICT applications in education were implemented as e-learning which has transformed education. The rise in Internet availability and the continual transformation occurring in software and telecommunication services has led to the ability to connect everything with anything. One of the first opportunities that arose was the concept of mobile and ubiquitous computing which has become the basis for mobile e-learning (me-learning) and ubiquitous learning (u-Learning). U-Learning has the potential to alter education in a sustainable manner and remove many of the constraints in education. This will allow customization according to students’ needs using embedded modeling and simulation (M&S) on demand. Hence, this paper presents an integrative concept for teaching with enhanced technology. 1. Introduction

With advances in information and communication technology (ICT), the development and expansion of the internet, the increase in the number of ICT applications useful for preparing educational materials, the availability of streaming video, audio, and animation processing software, and declining prices of computer hardware and other accessories, the role of ICT in education has been increasing in many countries. In the last two decades,

computer-based education (CBE), in its various forms and versions, has opened up the world of knowledge to everyone, leading to online education and e-learning in the mid-1990s. This includes all forms of electronically supported learning and teaching, and e-learning has become a platform that promises big achievements in educational programs. E-learning offers new ways for students to access many resources, a major breakthrough in education leading to better management of both in-house tertiary education and distance education. Therefore, the term e-learning is most likely to be utilized in reference to out-of-classroom and in-classroom education through technology, even as advances continue in regard to devices and curriculum. Abbreviations like CBT (Computer-Based Training), IBT (Internet-Based Training) or WBT (Web-Based Training) have been used as synonyms for e-learning. Moreover, the term e-learning is also used as a general term to refer to computer-enhanced learning (Holmes and Gardner, 2006). This includes the use of web-based teaching materials, hypermedia, multimedia material, websites, discussion boards and forums, computer-aided assessments, digital collaboration, or a combination of all of these methods. E-learning can be self-paced or instructor led and includes media in the form of text, images, animation, streaming video, and audio. In engineering education, e-learning plays an important role in different forms and at various levels. There are many universities offering online courses in all fields of engineering. Feisel and Peterson (2004), Feisel and Rosa (2005), and Gudimetla and Mahalinga (2006) provide feasibility studies about the effectiveness of e-learning in different engineering disciplines and recommend project specific organizational goals and benchmarks. In 2007, Vakilzadian and Möeller received a grant from the National Science Foundation (NSF) for the

Undergraduate STEM Education Initiative in Creative Educational Innovations for Electrical Engineering Students (USE-ICE), a course, curriculum, and laboratory improvement project. The goal of the project was to implement a modeling and simulation (M&S) degree program in engineering at the undergraduate level at the Department of Electrical Engineering in the College of Engineering at the University of Nebraska-Lincoln (UNL) to stimulate educational innovation and the development of skilled graduates for the public and private sectors. The project combines e-learning with the M&S approach within the same environment, helping students to better understand the complexity of today´s dynamic systems. The outcome of this NSF project has been published in several papers [Möeller and Vakilzadian, 2012; Möeller and Vakilzadian, 2010; Vakilzadian and Möeller, 2010]. However, in contrast to the increase in the utilization of e-learning, developing e-learning strategies and content, implementing courseware, and evaluating the outcomes poses difficulties and challenges that need to be addressed. Developing good courseware in engineering, which may involve automating design and construction processes, is a complex task and is hard to achieve. The reason for this is the lack of certain frameworks that are able to truly deliver essential dynamic learning products. In contrast, the availability of the Internet everywhere and rapid advances in ICT has led to the opportunity to connect everything with anything. This allows embedding e-learning on the mobile devices in the form of me-learning, a term that reflects the option of mobility in the e-learning approach. Traditional e-learning offers educational content as digital content on the web that can be viewed and printed through a browser. It includes mostly multimedia content and hyperlinks to access other sources and/or explanations and allows integration of additional graphics, video, audio, animation, and/or interactive simulation, enabling a better understanding of the complexity of the content as an additional value. A student can also study at a time and location of his/her choosing, enabling an independent form of learning with other learners and/or instructors. The fact that online education allows intensive interaction among students and with instructors is probably the biggest benefit of e-learning from an instructional perspective, since it allows access to resources and information anywhere in the world. With the advances in online learning technology, students can enjoy a rich range of interactions while benefiting from e-learning's flexible scheduling and user-directed pacing. It offers alternatives for meeting national needs for skilled professionals using time periods that are shorter than those in conventional teaching methodology [Möeller and Vakilzadian, 2012]. However, the strategy advocated now is to step outside the pure e-learning concept and introduce a hybrid

approach called blended learning (BL) that leads to better achievement and higher satisfaction than pure e-learning. In this approach, e-learning is combined with some form of human interaction. In engineering, blended learning is a combination of different modes and models of delivery and styles which also make use of the laboratory as part of an embedded collaborative virtual lab. The lab work is organized through the remote access approach [Möeller and Schroer, 2011]. To run the lab work through the remote access facility, students, called clients, access a web page, which activates the web server application of the lab work model, as shown in Fig. 1. When students are logged in, the web server redirects them to their actual lab work model. Data exchange occurs according to the simple request-response method. The HTTP client of the student sends a request to the HTTP server that processes it and returns the response. Students and server establish a connection via an interface for data exchange. Students can access the lab work and are allowed to change parameters and/or the lab work model itself. All experimental data can be exported for further study.

Fig. 1. Infrastructure scheme of web-based remote access

The changes through technological innovations of the past few years show a significant trend in the direction of more mobile use of the Internet, because modern equipment offers better options. Thus, the consequences of the state of the art in e-learning are: Past: access mostly through stationary personal

computers (PCs). Today: access via Notebooks, tablets (iPad, etc.),

MP3 players (iPods), and smartphones is on the way Tablets and smartphones show me-learning

content as „Website” or „App“ o Website: representation in the browser; meet

the technological options and constraints of modern browsers Pros: easy to achieve and is platform

independent o App: autonomous programs

Pros: use of smartphone/tablet resources

Cons: complex, native programming for each operating system (e.g., iOS, Android) necessary.

In general, the pros and cons of me-learning are: Pros: me-learning content can be offered

independent of time and location; new scenarios are feasible, such as the use of new time slots (bus, train, etc.); mobile units allow fast and easy to handle communication with other learners/ instructors, and more.

Cons: Smartphones have small displays which result in presentation restrictions; smartphones/tablets without WLAN means changing bandwidth reduces the possibility of adequate streaming, high resolution graphics, etc.; thus, downloading learning content to avoid online connection.

Blended e-learning must fulfill the requirements of today’s “digital native.” This term has been used in education [Bennet, Maton, and Kervin, 2008], higher education [Jones and Shao, 2011], and in association with the term New Millennium Learners, introduced by OECD in 2008. In this regard, a “digital immigrant” is an individual born before the existence of digital technology and who has adopted it to some extent later in life. If blended e-learning enables and supports learning within transportation systems, such as bus, train, car, airplane, ship, ferry, etc., it becomes the me-learning approach which requires new forms of content delivery. The user interface to broadcast learning object (LO) content in a web browser is only a part of the necessary improvements but the most important one because the user interface is the gateway between the learner (full-time students, part-time students employed in industry, engineers entering the job market, and midcareer engineers in industry) and the learning materials. Therefore, features from psychology and ICT have to be considered and summarized as “human-machine-interaction” which deals with the user-oriented design of interactive systems and their human-machine or user interfaces [Möeller and Sitzmann, 2012]. In short, the user interface must be designed in such a way that it is easy and intuitive to use, independent of the front end, as indicated in Fig. 2. Since people are different and feel different about "good," "beautiful," or "learning supportive" user interfaces for the online computer engineering (TIO) project, we created a widely adaptive layout. A first draft is given in Fig. 3 [Möeller and Sitzmann, 2012]. To accomplish the adaptive layout, a standard template, which defines basic features of the user interface, e.g., where the navigation or the content is placed, a good look and feel, has been developed. Working with the me-learning system, various properties are customizable depending on individual taste; colors,

fonts, sizes can be freely chosen. Moreover, it is possible to fade in or out other content elements, such as alternate representations, links, additional resources, surveys, chats, forums, etc. Thus, the user interface provides the potential for adapting the system to users’ needs.

Fig. 2. Front ends in me-learning.

Fig. 3. TIO e-learning user interface example of the online course “Embedded Systems.”

2. TEACHING WITH TECHNOLOGY

As previously mentioned, education has undergone major changes in recent years. The most relevant impact on education originates from the availability of mobile technologies which combine the functions of phones, cameras, multimedia wireless computers, and more. Mobile technologies enable the introduction of new concepts in learning, such as lifelong learning (LLL), an important approach to training the workforce of tomorrow by increasing their: • Creativity • Initiative • Responsiveness to facilitate adaptability by enhancing skills to:

• Manage uncertainty • Communicate across and within cultures, subcultures,

families, and communities • Negotiate conflicts The emphasis of LLL is on how to learn and how to keep learning for a lifetime. Thus, lifelong learning uses formal and informal learning opportunities, throughout people's lives to foster continuous development and improvement of the knowledge and skills needed for employment and personal fulfillment through: Learning to know: mastering learning rather than the

acquisition of structured knowledge. Learning to do: equipping people for the types of

work needed now and in the future, including innovation and adaptation of learning about future work environments.

Learning to be: education contributing to a person’s complete development: mind and body, intelligence, sensitivity, aesthetic appreciation, and spirituality.

In Table 1, the convergence between several learning approaches and technology is shown.

Learning Approach Technology Used

Learner centered User centered Situated Mobile Collaborative Networked Ubiquitous Ubiquitous Lifelong Durable

Table 1: Convergence between learning approach and technology used [Sharples, Taylor and Vavoula, 2005]. Using mobile technology learning can be regarded as situated, collaborative, ubiquitous, pervasive, and/or lifelong complementing the actual know how to become updated with the latest knowledge in the area of concentration. Moreover, mobile technology allows the sharing of knowledge with others, independent of their location. Thus, learning becomes ubiquitous with regard to the mobile technology embedded in most digital devices and/or units that perform human-oriented functions. These devices and/or units are also becoming more durable when it comes to storing content in whatever format makes it possible to build backward compatibility. This allows the preservation and organization of digital records of humans learning over a lifetime. The rise in Internet availability elsewhere and the continual transformation occurring in software and telecommunication services has led to the ability to connect everything with anything. One of the first opportunities to arise was the concept of mobile and ubiquitous computing, a term introduced by Mark Weiser in the 1990s [Weiser, 1993]. It refers to the process of seamlessly integrating microcomputers into the real world. Thus ubiquitous computing allows embedding

computing everywhere. Early forms of ubiquitous computing networks are evident in the widespread use of mobile devices, the number of which worldwide surpassed 2.5 billion in mid-2010. These little gadgets have become an integral and intimate part of everyday life for many millions of people.

As computers become ubiquitous, they cease being the focus of activity, which allow them to vanish into the background. Ubiquitous computing, however, includes computer technology which is available in digital cameras, microprocessors, smartphones, and other devices. Broadcasting ubiquitous computing into ubiquitous learning results in the interaction where students use their mobile technology gadgets to become connected with the manifold digital embedded devices and/or services. Therefore, in a ubiquitous learning classroom, students browse around the ubiquitous space built by mobile technology to interact with the various embedded digital devices and/or services. Thus, ubiquitous learning has the potential to enhance education in a sustainable manner and remove many of the constraints of traditional education, e.g., allowing customization in relation to student needs, building up the basis of a mobile-technology-based ubiquitous community. In this learning method, everything is:

• Traceable • Identifiable • Connected together, and smart phones are used as digital, low-cost computing and communication devices representing the platform. Thus, the platform capacity creates the concept in which sharing information between objects and devices connected to the ubiquitous space becomes a reality, a real constraint of u-learning facilitated through the Internet of Things (IoT) paradigm. IoT was first used by Kevin Ashton in 1999 and refers to uniquely identifiable objects/things and their virtual representations in an Internet-like structure. IoT is an approach linking a smart world with ubiquitous computing and networking, making different activities easier to attain. This can be achieved by sensors and actuators embedded in real-world objects linked through both wired and wireless networks to the Internet. When objects in the IoT can sense the environment, interpret data, and communicate with each other, they become tools for understanding complexity and responding to events and irregularities swiftly. Thus, the IoT is seen by many as a solution with insight into complex real-world processes. Started one decade ago as a wild academic idea, this interlinking of the real-world and cyberspace foreshadows an exciting endeavor highly relevant to researchers, corporations, and individuals. Thus, ubiquitous computing refers to computing which is embedded in every object/thing. Ubiquitous computing allows computing to

be embedded everywhere. In this sense, IoT offers a multitude of services, such as: • Person to person • Person to machine • Machine to person • Machine to machine • And more Thus, a u-Learning space (ULS) or u-Learning environment (ULE) can be introduced as a setting of pervasive education where data are present in the form of embedded digital mobile-technology-based objects. Objects or things are normally introduced as natural systems, physical systems, humans, sensors, actors, computers, and more. They just have to be there. Comparing u-Learning with me-learning, the level of embedding can be low while the level of mobility has to be high because me-learning is implemented in lightweight devices, such as smart phones, PDAs, and more, to become handy and usable anywhere. Internet access with wireless communication technology, however, is necessary to enable me-learning anytime and anywhere. Therefore, u-Learning requires embedded digital mobility technologies, as previously mentioned, because learners are moving around with their mobile devices and/or units. This necessitates dynamic learning support through communication with embedded computers in the learning environment [Ogata and Yano, 2012]. Developing a u-Learning space has to take into account the outcome of the existing learning theories in terms of best practices, such as a structured relationship between information and learners’ understanding in educational settings. This helps to prevent learning isolated from a meaningful context. For example, if a student understands why and how something happens rather than just being told that it is true, then the information is more relevant and, therefore, is more meaningful to the student. The rationale for this is that how is the inclusion of the pedagogical information; and why is the inclusion of interactive learning, allowing students to create knowledge from what they perceive. From a more technical perspective, the main hardware components of a u-Learning space are: Microcontrollers/computers: embedded in

objects/things, allowing the storing of information about objects/things.

Server: provides client stations with access to files and/or units and a database that stores all data about objects/units, users and interactions, as shared resources to a computer network.

Wireless communication technology: mostly in the form of Bluetooth and WiFi.

Sensors: used to detect changes in the u-Learning space; placed adjacent to objects/units and will be

used to recognize the presence of students in the u-Learning space.

Thus, u-Learning can be defined as being supported by embedded computer networks in everyday life based on the specific types of learning environments [Lyytinen and Yoo, 2002]. This development has allowed access to global communications and increased the number of resources available to today's students at all levels of teaching. After the initial impact of microcontrollers/computers and their applications in education, the introduction of e-learning and me-learning epitomized the transformations occurring in education. The mission of ubiquitous computing in education distinguishes another step forward with ubiquitous learning (u-Learning) emerging with the concept of ubiquitous computing. U-Learning is pervasive and persistent, allowing students to access educational material flexibly, calmly, and seamlessly. In this sense, ubiquitous learning has the potential for making education easier to achieve, removing many physical constraints of other forms of learning. Furthermore, the integration of adaptive learning with ubiquitous learning allows us to hope for innovation in the delivery of education, allowing customization to student needs. Adaptive learning itself is an educational method which uses computers as interactive teaching devices. In this regard, computers adapt the educational material to students' learning requirements, as indicated by their responses to questions and tasks. The seamless interaction between students and devices/units in ubiquitous learning can be introduced as follows: Students arrive and observe the object in the M&S

area of concentration. Adjacent sensors detect students’ presence and send data about the object to the student's mobile device/unit.

Objects will access the u-Learning environment server module and request information about the student.

However, being capable of both networked and independent operation, the object can operate alone and transmit data such as student information or data previously accessed, in a format suitable for that particular student using the u-learning environment server M&S module, and more. If the student has responded correctly to information in the past, this information will be transmitted [Jones and Jo, 2004]. This allows sending the required content to a student’s smart device and transmitting the student’s response to the u-Learning environment server component. The communication workflow is as follows: M&S Object No. 1 is accessed by Student No. 1. Information of M&S Object No. 1 is sent to Student

No. 1. Student No. 1 responds to information received. M&S Object No. 1 analyses the student's response

about information. No. 1 to identify the percentage of

understanding the M&S context. This will be done with the help of the u-Learning server module.

This information, e.g., Student No. 1 understands 55% of the M&S topic area of concentration, is relayed to all other objects in the u-space.

Let Student No. 1 now access M&S Object No. 2 which is aware of what Student No. 1 already knows about M&S and will attempt to explain some of the remaining content. Hence, Student´s No. 1’s interaction with the u-Learning space M&S objects during a u-Learning access (session) can be tracked and stored on the u-Learning M&S module server. If Student No. 1 joins the u-Learning space again, the system is aware of student No. 1’s knowledge gained. The student will be assisted in constructing a learning plan based on knowledge gained to date. This results in an enhanced learning experience and a deeper understanding of content in the M&S area of concentration. The availability of the Internet everywhere and rapid advances in ICT have led to the opportunity to connect everything with anything, whereby everything with anything consists of the M&S learning objects and the students. Thus, the M&S learning objects in the IoT based u-Learning space can sense the progress in individual learning, interpret data, and communicate with the student. Therefore, u-Learning has become a method of better understanding of system complexity and for responding to events and irregularities swiftly.

3. QUALITY ASSURANCE

The prime objective is to create a customized u-Learning process to make development, editing, and implementation sustainable. The central focus of the u-Learning approach lies in development and testing of various integrated processes including: A process for qualification of instructors (as a top-

down model) to gain experience in efficiently developing and implementing u-Learning materials within a short period of time with minimum cost.

Developing instruments to simplify the qualification, production, and implementation of u-Learning processes that will focus on didactics, appropriate technology, low cost, and sustainability.

Production of high-quality, u-Learning content (learning objects) to provide a venue for more effective learning by qualified teaching personnel, efficient utilization of the u-Learning course modules, and other instruments of u-Learners, and replication of the system and u-Learning materials to different learners in various learning scenarios.

4. M&S COURSE OFFERS

The Computational Modeling and Simulation (CMS) course sequence of two 30-hour course modules covers the required M&S topics, based on LOs. The LOs offer two teaching options: 1. face-to-face (f2f) with me-learning support

2. me-learning with f2f and u-Learning support The topics of the LOs of the CMS course modules are:

CMS I

LO No. Topic

1 Introduction 2 Modeling and Simulation 3 Continuous System

Simulation 4 Mathematical Description of

Continuous Time Systems 5 Simulation Software 6 Verification and Validation

CMS II

LO No. Topic

1 Discrete Event Modeling

2 Discrete Event Simulation 3 Stochastic Modeling 4 Stochastic Simulation 5 Simulation Software 5 Applications

To deepen the understanding of an application of CMS, a capstone project course has to be taken which is a weekly 3 hour workload. Working platforms for the capstone project are simulation software like MATLAB Simulink, ProModel, etc., for hands-on study. Thus, students can run their own CMS-based capstone project work solution and operate it as clients in a student team project. It has been observed that students become highly motivated, learning the basic and more advanced concepts of CMS and how these concepts can be applied to real-world complex systems and diverse smart entity applications in this way. This M&S program area of specialization has to be implemented by a set of agreed upon outcomes that prepare graduates to attain the program’s educational objectives, shown in the following table:

□ Ability to apply mathematics, engineering, science, and computing principles.

□ Ability to design and conduct experiments and to analyze and interpret the data.

□ Ability to design system, component, or process models to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability.

□ Ability to function in multidisciplinary teams. □ Ability to identify, formulate, and solve mathe-

matical, engineering and scientific problems by selecting and applying appropriate methods.

□ Ability to understand professional and ethical responsibility.

□ Ability to communicate effectively. Table 2: Selection of qualification criteria, following the ABET model.

To measure the expected learning outcome, key questions, as shown in Table 3, have been used to identify interactively whether students can go or not beyond the “what” and “how” of the content to include the explanation of “why.”

1. Describe what is meant by systems. 2. What is meant by continuous-time simulation? 3. Demonstrate an understanding of discrete-event system simulation. 4. Describe the difference between discrete-event and continuous-time modeling and simulation. 5. Describe the methodology beyond stochastic simulation. 6. Why do we need verification and validation of simulation models?

Table 3: Expected learning outcome. 5. OUTLOOK

The planned u-Learning space in the M&S domain will use entity-entity oriented IoT services. The reason for that is that the IoT services can be monitored in real time. Hence, dependent of their actual status, the IoT services automatically can react through IoT-enabling technologies such as: • RFID: Radio-frequency identification devices, so

called tags, are a prerequisite for the IoT and u-Learning application. So far, the objects of the M&S u-Learning space are equipped with tags that can be identified and tracked by the IoT computers.

• Sensor technology: opening new frontiers for improving the processes in the M&S u-Learning space. Thus, installing seas of sensors allows a much greater granularity monitoring student interactivity.

Therefore, the main features of the planned M&S u-Learning space, besides others, are: • User data base with learner’s profile containing

name, age, gender, study program, area of concentration, interests, etc.

• U-space data base with data of LOs, rooms and buildings, links between LOs, and expressions in the LOs.

Hence, this planned u-space environment will allow students to access the M&S u-Learning space in various contexts and situations using their RFID-based wild cards. The M&S u-Learning space will also examine students’ knowledge and include the comprehensive level in the LOs. In addition, the u-Learning space will be able to detect a learner’s comprehension during system use, giving advice to the learner and instructor, if enabled. Therefore, in the u-Learning space, students are immersed in the learning process. This can be interpreted as pervasive learning because the LOs are all around the student, but the student may not even be conscious of the learning process. They must only be there.

6. CONCLUSION

The concept of u-Learning goes beyond portable computers. It can be seamlessly embedded into a u-Learning space and will become the major teaching technique using the technology paradigm. Thus, using u-Learning course modules is an ideal problem-oriented learning strategy.

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AUTHORS BIOGRAPHS DIETMAR P.F. MÖLLER is a Professor for Stochastic Models in Engineering Science at Clausthal University of Technology (TUC), Faculty of Mathematics, Informatics and Mechanical Engineering, Germany. He is also a Professor at the University of Hamburg, Germany, and Director of the McLeod Institute of Simulation Sciences at TUC. His current research interests include aeronautical and space engineering, computational modeling and simulation, embedded intelligent systems, hardware-software co-design, me-and u-Learning, multimodal transportation and logistics, robotics, transportation system analysis, modeling and simulation.

ROLAND E. HAAS is a Professor for Embedded Systems at the International Institute of Information Technology Bangalore (IIITB), India. His current research interests include automotive IT, embedded systems and advanced product data management, engineering workflow automation, concurrent and simultaneous engineering as well as knowledge-based engineering. He is also interested in the architectural and performance aspects of complex software systems as well as design issues of mechatronic and embedded systems.

HAMID VAKILZADIAN is an Associate Professor for Electrical Engineering at the University of Nebraska-Lincoln (UNL). He is a Region 4 Technical Activities Chair of IEEE. His current research interests include computational modeling and simulation, microcomputers, logic design and analysis, embedded systems, and curriculum development.