Keywords

1 Introduction

In the past decade, we have experienced the development of mobile devices, techniques, and networks. During this period, learning and teaching have been moving from indoors to the integration of both indoors and outdoors with the help of mobile devices and mobile learning materials (Hung, Lin, & Hwang, 2010). Moreover, with the explosion of new knowledge and learning resources, one could not keep isolated from the new learning environment. Accordingly, in order to adapt to the era of knowledge economy and the meet the requirement of lifelong learning, one had better to learn new things related to his/her profession or interest (Min, Sheng, & Feng, 2016). However, on the one hand, knowledge from textbooks or other kinds of publication are not so immediate in reflecting the development of specific fields. On the other hand, textbooks are much more expensive if you want to follow the latest work. Under this condition, there are more and more online learning materials developed for learners and instructors to conduct the learning and teaching process on the online learning platform, especially on mobile learning platforms (Chen, Feng, Jiang, & Yu, 2016). From the results of many researches, it has been demonstrated that online-based or mobile-based learning has achieved great success in helping students in their knowledge construction, improving learning performance and motivating learning attitude (Hung et al., 2010; Chu, Hwang, Tsai, & Tseng, 2010; Chu, Hwang, & Tsai, 2010). Therefore, many more researchers have adopted mobile learning in their educational innovations and tried to explore some new forms of learning practice for future application (Lai, Hwang, Liang, & Tsai, 2016; Tan, Ooi, Sim, & Phusavat, 2015; Huang & Chiu, 2015). From all that is mentioned above, we have a preview of the effectiveness of mobile learning. But how can mobile learning play this role in the learners’ learning process? Many of the reasons could be attributed to the technologies embedded or integrated adaptively in the mobile learning platform on the basis of some learning theories (Ally & Prieto-Blázquez, 2014; Keengwe & Bhargava, 2014). So what technology could be essential for mobile learning or what technology helps students learn with electronic resources more easily and efficiently? For example, personalized recommendation technology had helped students to locate learning resources more accurately (Yang & Yang, 2015; Hsu, Hwang, & Chang, 2013; Wang & Wu, 2011), and contextual technology (Chen & Huang, 2012) could help students adopt the most adaptive learning resource in a specific learning context. So, in this chapter, we present and introduce the core technologies which have been widely used in mobile learning platforms. What’s more, some new technologies that could be helpful in improving the experience of mobile learning will also be discussed.

In order to know what technology would be crucial for mobile learning clearly, we should first know the definition and the features of mobile learning. Thus, in the second section, the definition and features of mobile learning are presented. The third section introduces some related works on technologies in mobile learning. The next section is the main part of this chapter, and it will discuss the core technologies used in mobile learning. Accordingly, in the fifth section, the author illustrates some examples to better understand how these technologies affect the learning process. Finally, there will be a discussion of prospects and conclusion.

2 Definition and Features of Mobile Learning

The concept of mobile learning had been proposed around 2000. From Google Scholar, if you search with the keyword ‘mobile learning,’ you could get almost nothing related before 2000. The earliest definition came from Quinn (2000) in his paper ‘mLearning: Mobile, Wireless, In-Your-Pocket Learning’ in 2000. At that time, mobile learning had been defined as ‘e-learning through mobile computational devices.’ As time went by, many researchers redefined mobile learning. Sharples, Taylor and Vavoula (2005) defined mobile learning as a state distinguished from other types of learning in which learners were continuously on the move, both in the time and the space. Traxler (2005) defined it as “any educational provision where the sole or dominant technologies are handheld or palmtop devices.” Later in 2007, Traxler improved the definition, adding the properties of “learners’ experience, ownership, informality, mobility and context,” as the previous one focused too much on technique (Traxler, 2007). Thus, most of the definitions emphasize on the mobility of the learners. In recent years, researchers tend to consider some other perspectives in learning, such as personality, contextualization, socialization, and knowledge building. Yu (2007) defined mobile learning as the following: mobile learning is a process for learners to get access to any learning resources with any device (mobile phones, PDAs, iPads, or other kinds of device with wireless communication modules) at anytime, anywhere. During this process, learners can communicate and collaborate with other social learners through networks in order to accomplish the construction of knowledge in learners’ minds. As for this chapter, we prefer the last definition, and think of mobile learning mainly as a state with intelligent services in the learning process. The state is mobility and the intelligent services help learners who are in a mobile state acquire adaptive content, resources, peers, and even instructors.

Apart from the definitions of mobile learning, to better illustrate the concept and make the definition fit the learning environment, many researchers describe mobile learning with some features. In Yu’s (2007) research, mobile learning has been tagged with 3 generations. In each generation, the emphasis is different. In the 1st generation, mobile learning emphasized the transfer of knowledge among learners. In the 2nd generation, the focus is cognitive construction, followed by the 3rd generation emphasizing situated cognition. Accordingly, the features of mobile learning have been reformed from support mobility, informality, accessibility for mobility, personalization, interactivity, multimedia, accessibility, superlinking, and connectivity (Sharples et al., 2005). Moreover, with the situated cognition illustration of mobile learning, the features of mobile learning have evolved to underline situated personalized learning. So during this period, the features of mobility, ubiquity, continuity, adaptability, connectivity, and being social, contextual and multi-mode. Each feature is described as follows:

Mobility: mobility is the most important feature of mobile learning because it defines the state of learning. Learning should not be confined to a fixed device or context.

Ubiquity: the feature of ubiquity is a supplement of mobility and is not so consistent with what we know as ubiquitous learning. It is just the narrow meaning of ubiquitous, meaning that learning can take place with any mobile device, at anytime, anywhere. That is a 3A (any content, any time, any location) learning with another confined A (any ‘mobile’ device). During this process, students can learn what they are interested in or need with mobile devices such as mobile phones, iPads, PDAs or wearable devices either in classroom or out of classroom.

Continuity: learning can be a kind of embedded learning integrating classrooms, workplaces, and any other places. Under this condition, one can link the in-class content with the out-of-class content seamlessly. This feature breaks the boundaries of informal and formal learning.

Adaptability: this feature extends personalization. With adaptability, each student can be a different individual. What we want to provide is not different services for different individuals but adaptive services for specific individuals. The services include learning materials, resources, helpers, and online support services. With all of these adaptability, we can benefit all learners in their mobile learning.

Connectivity: From the theory of connectivism, all factors in learning can be connected to other factors. This feature extends accessibility, meaning that learners can connect to learning resources while they can also connect to similar learners. With all these connective relations, learning can be a connective network.

Social: learning is not an isolated process. Communication with peers can be helpful in individuals’ learning. Each knowledge or learning content can be related to some other learners or experts. When interacting with them, one can find the social knowledge network behind the content. So mobile learning is not only a static isolated process but also a dynamic social process.

Contextual: while learning is not an isolated process, learning can be conducted in specific contexts. The definition of context is not confined to the physical situation of learners. It also refers to other contexts such as learning context, instruction context, and emotional context. These contexts are factors influencing students’ learning achievements.

Multi-mode: this feature means the form of learning can be multi-mode. Not only physical behavior will be input to evaluate, but also other forms of behavior such as emotional behavior and machinery behavior will be transferred to the mobile learning platform for further evaluation.

Knowing the above-mentioned features of mobile learning, we can understand the definition and process of mobile learning more clearly. Owing to this aspect, we can conduct mobile learning projects, courses, or experiments more easily. In the next section, we will discuss the related works in the technology-enhanced mobile learning field and list some of the challenges accordingly.

3 Related Works and Challenges in Technology-Enhanced Mobile Learning Contextual Multi-Mode

Technology-enhanced mobile learning simply means to use proper technology to help the implementation of mobile learning and to make the learning process more meaningful. Moreover, it aims to facilitate learning based on the above-mentioned features of mobile learning. There are many cases and experiments with technology-enhanced mobile learning that have been conducted all over the world. Some of them provided application examples for researchers and practitioners.

For mobile learning or ubiquitous learning, they all emphasize mobility and all-around learning. Learning can take place at anytime, anywhere, and with any device. The feature of mobility and ubiquity make it possible for students to make full use of spare time such as when he/she is at the subway or bus station. Moreover, this feature helps learners break the boundary between formal and informal (or indoors and outdoors). Learners can study seamlessly and continuously with the content he/she has just learnt in the classroom when it is time to go home. Chen et al. (2003 ) developed a mobile learning system to scaffolding the students with the bird-watching which made the learning process mobile. The result of this study shows that students using the system experienced an improvement in the learning outcome. Shih and Hwang (2010) conducted an experiment with RFID in learning about butterflies and wetland resources. The students used a PDA to scan the RFID tag to acquire the learning content. The result shows that this method can be helpful for students’ learning as well as knowledge construction. Moreover, the learning process can be continuous. In these studies, mobile technology and wireless technologies are utilized.

In recent years, adaptive learning has been quite popular in education. For adaptive learning, not only the content should be adaptive, but the environment, format, and devices should also be adaptive. Some research on adaptive learning has demonstrated the effects of it. For example, Tseng (2008) created an idea of two-source personalized information for an adaptive learning system which analyzed learners’ learning styles and behavior. With this idea, the researchers developed a mobile learning system and conducted an experiment with it. The result shows that it is of benefit for students’ learning. This case can also reflect the multi-mode feature.

Connectivism (Siemens, 2005) supports that learners, instructors, and learning resources are all particles in the learning spaces. Learning is to connect these particles so as to form a network. It is also a feature of mobile learning. Thus, making learning a social process and having more people involved in it is very important in facilitating learning. Students’ collaborative learning and teachers’ collaborative lesson planning are all cased for connectivism. Wang et al. (2015 ) conducted an experiment on teachers’ interactions in lesson planning and viewed the result of the lesson plan. The result shows that meaningful collaboration in CLP will be helpful for teachers’ personal skills.

Contextualization is quite an important feature for mobile learning. It helps learners acquire resources adaptively for the present context. Huang, Liu, Lee, and Huang (2012) conducted a context-aware experiment. They adopted a nursing course as a subject and developed a mobile platform integrating the course content into the mobile devices to help nurses to study and act properly in real workplaces. The result shows that learners’ learning outcomes have been improved.

From what have been mentioned above, we can see that some of the technologies have been used to support technology-enhanced mobile learning. They are used to create the mobile space, provide social connections, build adaptive learning environments, and better support personalized and contextualized learning. These applications have had some good effects and provided us some references. However, there are still some challenges for us to focus on in mobile learning. For example, though there are some good cases for mobile learning, there are rarely good mobile apps that can support large-scale learning based on learning science theories. Secondly, the context-aware cases mainly used QR code or RFID to support learning and therefore are not very efficient. Thirdly, most mobile-supported personalized learning provides contents preset in the database, so it could not adaptively support the dynamic context the learners may be confronted with. Moreover, in some majors, we still need environments which can support learners’ authentic experience. Finally, the lack of student data for an efficient analysis is a crucial problem in mobile learning. Recently, the development of some technologies has been very helpful in solving these problems. In the next section, we will discuss the core technologies used in mobile learning.

4 Core Technologies in Mobile Learning and Their Applications

From what has been mentioned above, we describe some core technologies that may play an important role in the conduct of mobile learning. To fulfill the mobility and continuity features, mobile application technology is proposed. For ubiquity, there are many technologies that will support it, such as location-based service, VR/AR, and wearable technology. Personalized recommendation technology will support the adaptability feature. The connective and social features can be achieved by social knowledge networks as well as data- and relation-driven ontology behind the network. The feature of contextualization can be resolved by context-aware technology. The last feature, multi-modality, can be fully realized through artificial intelligence and big data technology. The core technologies in mobile learning are listed below: mobile application development technology; location-based service; VR/AR; wearable technology; personalized recommendation; social knowledge network (intelligent aggregation; linked data); ontology; context-aware technology; artificial intelligence; big data.

Mobile Application Development Technology: Mobile application development technology is not the program technology and hardware or network supporting the function of the apps. It includes the learning framework supporting various learning environments. As learning is an activity of both behavior development and cognitive development. Different people have different cognitive structures, and the structures are not as easy to represent as the process of purchasing (Albert, 1993). Thus, in developing mobile learning apps, we should first design the cognitive framework of specific subjects according to learning science theory (Thelen & Smith, 1995). After that we can integrate the framework into the development of learning apps. After the apps for several subjects are produced, we can form a mobile learning app framework supporting students’ whole learning process and subjects.

Location-based Service Technology: Location-based service is another important technology in mobile learning. As mobile learning is mainly conducted outside the classroom, location-based service is quite important in telling what context the student is located in. Accordingly, the mobile learning system can provide specific guidance for the student based on the preset learning path. Many researchers have explored the effect of location-based learning (Hsiao, Lin, Feng, & Li, 2010; Unwin, Foote, Tate, & Dibiase, 2012; Choi & Kang, 2012) in environment learning, language learning, and support collaborative learning. Results of the research demonstrated that location-based learning can be helpful in students’ learning outcomes as well as motivation (Hsiao et al., 2010; Choi & Kang, 2012).

VR/AR: in recent years, VR/AR become quite a popular topic in mobile learning. VR/AR can integrate the virtual context with the real context. The learners can get an authentic experience. This is an advantage in the learning process. Traditionally, students can only learn with given material in a given context, and this has led to a gap between real world and the classroom. Thus, it is quite difficult for many students to transfer knowledge into different contexts. VR/AR make it possible for students to learn with authentic experiences in an integrated context. Much research on VR/AR shows that it can benefit students’ learning and attitudes (Hussein & Natterdal, 2015; Cai, Chiang, Sun, Lin, & Lee, 2016).

Wearable Technology: Wearable technology is another mobile learning technology. Wearable devices can sensitively get information from users and environments. With the information, the mobile learning system can locate the state of the learner. For example, if a student wears the device for a long time, the device can gather the behavior the student performed during this period. With this information, the mobile learning system can have an overview of the student’s state, such as when will the student be tired, when will the student will be excited, if the student is sick and what was the student learning when the student was excited or tired. The state information can help the teacher or the system to intelligently analyze the student’s state, preference, and to give personalized feedback (Pirkl et al., 2016). Further, we can also use deep learning technology to model the behavior (Zhu, Pande, Mohapatra, & Han, 2015). There is little research on this topic now.

Personalized Recommendation: Personalized recommendation is one of the most important technologies in mobile learning. In mobile learning, what we need is mostly from the recommendations of the mobile learning platform. From the theory of learning science and multiple intelligences, learners have different intelligences, and when they are learning, they perform differently and need different learning content and learning paths (Gardner, 1999). Using personalized recommendation technology to provide different resources, content, peers, instructors, and services for students could be helpful for their learning (Wang & Wu, 2011; Huang et al., 2012). Presently, the algorithms and models behind the recommendation are basic. The algorithms include collaborative filtering recommendation, content-based recommendation, knowledge-based recommendation, and other recommendations based on Bayes or IRE (Item Response Theory). The construction of the user model will mainly be decided by several attributes such as the learning style, subject, and the learning stage.

Social Knowledge Network: Social knowledge network is a technology to represent the relation computed by the server. Traditionally, the learning platform usually provides the relation among users or among knowledge. SKN provides a method for mobile learning systems to graphically show the relations among users and knowledge. If a learner has some problems in learning, he could find specific experts on the problem as well as specific resources. From particular users, we can also find resources relating to them. Behind the SKN, ontology technology, linked data technology, and aggregation technology are used to output the SKN service. Ontology and linked data are knowledge structures in the system. Aggregation technology can gather similar relations to provide visualized output. With these technologies, knowledge can be represented as triples (concept, property, and relation). All the visualized relations in SKN can be found or reasoned through the ontology and linked data. Research showed that SKN will be helpful in students’ learning navigation and learning outcomes (Li, Zheng, & Jing, 2015).

Ontology: Ontology is a technology that can represent knowledge and the relations among knowledge (Ferreira-Satler, Romero, Menendez, Zapata & Prieto, 2010). With these relations, we can reason according to what we need. For example, if Yu is an expert on mobile learning in China, Ally is an expert on mobile learning in Canada, Ally is responsible for a journal in Canada, Wang is Yu’s student and Wang has submitted an article to a Canadian journal. With the ontology, the mobile learning system will easily reason Wang probably submitted the article to Ally’s journal. However, in a traditional platform with keyword search, we could not find the relation easily. Thus, ontology is quite an important technology to help create dynamic relations among resources.

Context-aware Technology: Context-aware technology is one of the most widely used technologies in mobile learning. It is mainly based on location-based service and wearable devices or sensors embedded in mobile devices to sense the learning context. The learning context contains several parts: user physical context, user learning context, user instruction context, time–space context, and resource context. Physical context describes the physical information of learners; user learning context describes the learning history or state of the learner; instruction context describes the instruction information such as the adaptive teacher or teaching style; time–space context describes when and where the learner will learn the most efficiently; finally, the resource context describes the adaptive context for the resource. With all of these context descriptions, users can get the most adaptive learning resources and services (Chen et al., 2016; Min et al., 2016).

Artificial Intelligence and Big Data: in the past several years, big data and artificial intelligence have been quite popular technologies; they have attracted many people’s attention. Big Data is a data collection and analysis technology. Like SKN, it needs data from learners’ daily learning behavior. Also for artificial intelligence, if we can collect enough data, we can use this data as samples to train some models under specific contexts of learning. During the training, a deep neural network is used to create prediction relations like human’s brains. If the model is trained with comparable high accuracy, it could be used to predict a new learner’s behavior or learning outcome. For this reason, we can use artificial intelligence to predict students’ learning path and recommend them to learn with the most adaptive one. In this field, there are not very many researches focusing on the improvement of learning.

All these techniques help provide more adaptive services for mobile learners. Firstly it enhances the outside learning envrironment and then it enhances learners’ interactions with learning content. Also during their learning process we can sense their learning environment, extract their personal preferences, collect their learning behavior, analyze their learning condition, provide adaptive learning activities or interactions and lastly give them learning feedbacks or suggestions. Moreover, the learning process becomes authentic and immersive instead of interacting on the surface. It would benefit the learners a lot.

5 Framework of Mobile Learning Technologies Application

With all the theories and technologies we could have a better understanding of mobile learning. However, in conducting mobile learning there are still some problems for learning is not only to provide technologies and services but also to provide the right person the right things. Mobile learning is a process involves many factors such as learners, learning platforms, devices, environments, learning resources, interactions and peers and experts. Without taking all these factors into consideration, some problems may occur when conducting mobile learning and our efforts may be wasted. So using the theories and technologies above to provide a framework for mobile learning would contribute to the practitioners. And in the framework mobile learning technology can help solve some problems in the mobile learning context. Figure 8.1 shows the framework of it. It is composed of an Educational Cloud Computing Center, Personalized Learning Services, Extended FOAF-based Social Networks, Adaptive technology-based presentation, Ontology-based knowledge base, and a Contextual interface. All of these parts help to form a work flow in mobile learning. When a learner is learning with a title under specific context and confronted with some problems, the context-aware interface will firstly sense the learner’s context using the sensors embedded in the devices or equipped in the environment and become aware of what the learner needs and then collect the context information, personal information, learning information, and other related information. All information will be transported to the Educational Cloud Computing Center. In this center, with big data technology, AI technology and semantic technology, what the learner needs, such as activities, resources, SKN, tools, and any other kind of learning services, would be aggregated. After that the content would be adaptively presented with proper devices. Further, the interactions between the learner and the aggregated content would be recorded to support the learning resources’ evolution and the generation of new relations between learners, resources and ontology which is behind the resources and learners but play an important role during the process. After that a individual portrait, social knowledge network (including the learners’ network, knowledge network and their blended network) will be formed for the learner and the behaviors generated will be reserved for future use. This process informs the framework which can integrate the main mobile learning technologies to facilitate learning.

Fig. 8.1
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Framework for mobile learning technologies

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6 Prospects and Conclusion

With the development of mobile technologies and networks, mobile learning has become a quite general learning form. Much research is based on mobile learning technologies to improve learning, such as context-aware, personalized recommendation, location-based service, and wearable technologies. Moreover, they have achieved some good feedback for learning. They are of importance in improving our learning and teaching. In the future, new technologies such as ontology, SKN, big data, and artificial intelligence will make great influence in the learning area.