Selection of Ubiquitous Computing Technologies and Environments as Performance Improvement Interventions Ilker Yakin, Mersin University, Turkey Ayse Gunay, Yildiz Technical University, Turkey Abstract: Ubiquitous computing environment constructed by wireless and mobile technologies as a new trend might produce intended results for closing performance problems for different settings. In this paper, the researchers tried to explore adaptability and efficiency of ubiquitous computing technologies and environments to solve performance problems in Human Performance Technology (HPT) perspective. To serve that purpose, the U-PI Model was developed in accordance with the HPT context and its components were discussed in detail. Using the U-PI Model, the proposed ideas might have a contribution to the field and an implication for the practitioners and researchers concerning the selection, designing, and development of ubiquitous computing technologies and environments in any performance improvement initiatives. Keywords: Human Performance Technology, Performance Improvement Interventions, Intervention Selection, Performance Factors, Performance Drivers, Performance Solutions, Conceptual Model Development
Introduction
T
o fulfill today’s complex performance needs and demands, organizations should give place to a more complete Human Performance Technology (HPT) so as to strengthen training programs and provide required supports for employees with variables which have an effect upon their performance (Elliott, 1998; Hotek and White, 1999). Sustaining a competitive edge for companies, more organizations examine the increasing role of HPT and performance improvement processes (Elliott, 1998). Because an attainment of valuable and desired results is based only on systemic and systematic design, development and implementation of performance improvement systems, multiple performance technologies and HPT should serve as a basis for these vital demands formulated by the organizations (Watkins, 2007b, 2007c). Besides management perspectives, we also live in a world of permanent change regarding computing systems. Along with the emergence and dissemination of mobile devices, the paradigm shift from traditional computing to a new ubiquitous computing has become a fact for the information and communication technology field. Different from the traditional desktopbased computing, today there is growing excitement about a new ubiquitous era that may be further integrated into our lives (Loureiro et al, 2007). As a consequence of these advancements; therefore, tomorrow’s organizations will be substantially different from today’s. Recent advances in innovative and seductive computing technologies have revealed that performance technologists should be willing to ascertain and use these innovations while conducting performance improvement efforts and projects. The purpose of this paper is twofold. Firstly, it attempts to explore adaptability and efficiency of ubiquitous computing technologies and environments to solve performance problems in HPT perspective. Secondly, it aims at developing a model – The UPI Model- to assist researchers and practitioners with the selection process of the ubiquitous computing technologies and environments in performance improvement initiatives and projects. To serve that purposes, the paper is organized as follows: Section two outlines ubiquitous computing technologies and environments. Section three highlights the definition, basic phases of the HPT,
Ubiquitous Learning: An International Journal Volume 6, 2014, www.ubi-learn.com, ISSN 1835-9795 © Common Ground, Ilker Yakin, Ayse Gunay, All Rights Reserved Permissions:
[email protected]
UBIQUITOUS LEARNING: AN INTERNATIONAL JOURNAL
and more specifically, intervention selection processes via presenting performance factors. The main components of the U-PI model is demonstrated and visualized by constructing connections between specific performance factors in the third section. The forth section of the paper outlines conclusions and recommendations relative to the utilization of basic components of the model for further studies.
Ubiquitous Computing Technologies and Environment The last decade has witnessed dramatic technological improvements in the ubiquitous computing. In 1991 Mark Weiser coined the term “ubiquitous computing” to perfectly describe a technological innovation that would result in the pervasiveness of devices in our lives (Perrotta, 2011). With the advancement of innovative hardware and networking technologies, researchers become more ambitious for discovering technological advances that have leveraged the development of pervasive systems (Loureiro et al, 2007).To illustrate, powerful mobile devices and wireless networking technologies such as Wi-Fi and Bluetooth allow designers to develop much more complex mobile applications that lead up the vision of ubiquitous computing (Loureiro et al, 2007). As traditional computing turns into ubiquitous computing, its potential turns into ubiquitous learning (Ng et al, 2010). The ubiquitous learning have been defined as a paradigm that learning process can be context aware nature via providing users to reach information access in the right time, place and form (Restrepo et al, 2013). In the literature, it has been also defined as a learning approach that mobile, wireless communication and sensing technologies are utilized for making learning supports for users in real-world environments (Hwang et al, 2008). By stating these technologies in the definition, many ubiquitous technologies such as smart mobile phones, handheld terminals, sensor network nodes, smart cards and RFID (Radio Frequency Identification) are employed in u-learning environments (El-Bishouty et al, 2010). Using these technologies, students are provided with some opportunities such as accessing digital materials and receiving feedback in real settings (Tsai and Hwang, 2012). Ng et al (2010) proposed six crucial ubiquitous learning requirements that include the ability to facilitate seamless learning, the ability to access files and documents from anywhere, the ability to acquire information, the ability to provide interactions between teachers, peers, or experts, the ability to situate instructional activities, and the ability to provide students with the right information whenever needed in accessible ways. In doing so, one of the major advantages of the ubiquitous technologies and environment is to offer and provide intelligent actions and relevant information (Loureiro et al., 2007) for users by interacting with devices and applications. Whenever needed, users can reach pertinent information or services in an accurate format. Undoubtedly, research in the ubiquitous computing, learning and technologies has considerably advanced. If these efforts are considered from both personal and organizational performance points of view, the shared understanding and interdisciplinary studies might be established on the subject of both ubiquitous learning and performance technology.
Human Performance Technology (HPT) The HPT as a field and practice is mainly based on different research and evaluation approaches coordinating the research questions and methods based on them (Pershing, 2006; Stolovitch, 2000). Although many research bases lie behind the field, practical experience and scientific research conventions must direct any HPT efforts to generalize its own specific laws and experiences (Stolovitch, 2007). HPT is defined as processes and tools with the aim of improving and enhancing individual, group, and organizational performances. Therefore, its principles and approaches can be applied within any organizational, work, and social improvement settings (Dick and Johnson, 2007).
2
YAKIN AND GUNAY: SELECTION OF UBIQUITOUS COMPUTING TECHNOLOGIES
HPT comes to be known as a field of practice in the 1970s (Chyung, 2008; Stolovitch, 2007). Today, all sizes and types of organizations, such as private businesses, government, social service, and nonprofit organizations, educational institutions, and the military may use a HPT for all their performance challenges and problems (Pershing, 2006).HPT is a field that incorporates processes which begin with desired results. Moreover, the main objects of these processes are to generate value for an organization itself, its employees, and the society it attends (Pershing, 2006). More specifically, the purpose of HPT at workplace is to make the organization better through improving performance to produce the desired results (Addison and Haig, 2006; Brinkerhoff, 2006; Van Tiem, Moseley, and Dessinger, 2001). In other words, the ultimate goal of HPT is to solve complex performance problems originated in the organization and direct the organization in a positive way (Van Tiem, Dessinger, and Moseley, 2006). Simply put, HPT follows three main systematic processes. The first process for achieving goals is to analyze performance problems and their underlying causes. The second process comprises identification and implementation of solutions. The third process is on about evaluation of results for the organization (Van Tiem et al 2001).To succeed in improving performance of any settings, performance analysis, cause analysis, intervention selection, design and development, intervention implementation and change, and evaluation have been used as essential phases in the HPT(Van Tiem et al, 2001). Among these phases, intervention selection as a strategy plays a vital importance so as to decide on the types and number of interventions to solve performance problems. Although many classifications of interventions to be selected and used for any performance improvement initiatives has been proposed in HPT (Van Tiem et al 2004), the frameworks should be expanded with the advancement of new and innovative technologies.
Intervention Selection in HPT The term intervention was coined by Barry Booth and Odin Westgaard in 1979 (Hale, 2007). Intervention is a performance initiative aiming at improving the organization’s efficiency and effectiveness (Miles, 2003). Van Tiem et al (2001) define interventions as improvement activities which are used for fixing and depletion of problems emerged in the workplace. According to Stolovitch and Keeps’ (1998) view, intervention is a solution or a solution component determined for closing the performance gaps. Literature on performance analysis shows that selection of the justifiable performance solutions depend on analyses results. Findings from analyses might help practitioners make appropriate and useful performance improvement decisions (Watkins, 2007b). As Svenson (2006) defined this process as “deriving requirements”, intervention selection categories and also analysis data play a pivotal role in this stage. Especially, performance analysis is important for deciding on the types and number of interventions (Watkins, 2007a). Having identified the performance gaps with the performance analyses, interventions should be designed for either balancing the performance levels or closing all performance gaps (Desautels, 2006). After performance and cause analysis phases, some interventions could be recommended by HPT practitioners.
Classifications of Interventions and Performance Factors In HPT, there are many classifications of interventions to be selected and used for any performance improvement initiatives (Van Tiem et al, 2004). Van Tiem et al (2001, 2004) divide the intervention selection process into three phases: preliminary, survey and selection so as to handle procedures to be more manageable for practitioners. For this purpose, they develop a performance intervention tool based on most common interventions and classify these by relationships among each intervention. Their classification consists of eight possible categories: performance support systems, job analysis/work design, personal development, human resource
3
UBIQUITOUS LEARNING: AN INTERNATIONAL JOURNAL
development, organizational communication, organizational design and development, financial systems, and other (Van Tiem et al, 2001). Without giving any links with causes of performance, Stolovitch and Keeps (1998) divide interventions into two categories, learning and non-learning. According to their categorization, learning interventions are appropriate and should be used when there is a lack of skills and knowledge in the organization. To help performers acquire skills and knowledge, on-the-job training, simulation, role play, natural experience, laboratory training and classroom training are examples of learning intervention (Stolovitch and Keeps, 1998). As for non-training interventions, they can be classified as job aids, environmental and incentive, consequences and motivation (Stolovitch and Keeps, 1998). Likewise, Van Tiem et al (2001, 2004) classify performance support systems as instructional and non-instructional according to their potentials, addressing individual and organization needs that they comprise. Watkins (2007a; 2007c) offers a framework called the “Performance Pyramid” for organizations. Watkins’ (2007a) framework is based on the idea that each or cluster of performance factors should be associated with related performance technologies given in the system (Table 1) as a set of performance solution packages. The subject framework provides a direct link between the relationships and solutions (Watkins, 2007c). Watkins (2007a) goes beyond the idea that each performance technology should be connected with the results of the performance and cause analyses before the design and development stages. Table 1: The Performance Pyramid with Associated Performance Technologies Building blocks of Performance Associated Performance Technologies Strategic planning, needs assessments, balanced Strategic Direction scorecards, communication opportunities Communication opportunities, performance reviews, Expectations and Feedback balanced scorecards, participation in strategic planning Computer systems, workplace redesign, process Tools, Environment, and Processes engineering, ergonomics review, communications Rewards, Recognitions, and Awards program, communications, monetary Incentives incentives, balanced scorecards Mentoring, career counseling, motivation workshops, Motivation and Self-Concept team-building programs, performance appraisals Recruitment programs, retention programs, resources Performance Capacity allocations, workforce planning, new computer technologies Job aids, classroom training, e-learning, mentoring, Skills and Knowledge just-in-time training, after-work educational opportunities, knowledge management Source: Watkins, 2007a Rosenberg (1990) offers a general schema that all possible interventions fall under any performance levels (Table 2).
4
YAKIN AND GUNAY: SELECTION OF UBIQUITOUS COMPUTING TECHNOLOGIES
Table 2: Performance System Factors Examples Performance criteria/feedback, job documentation/job aids People, money, equipment, time Organizational, job, ergonomic efficiencies
Factor Data Resources and Tools Skills and Knowledge
Training and education
Factor
Examples Compensation, New opportunities Career planning/development Personnel selection Succession planning Quality orientation Empowerment, leadership Source: Rosenberg, 1990
Consequences, Incentives and Rewards Capacities Motives and Expectations
Rossett (2009) offers a general solution framework for each kind of performance drivers. According to the framework (Table 3), four performance drivers, skills, knowledge and information, motivation, environment and incentives, are associated with probable solutions.
Performance Drivers
Table 3: Summary of Drivers and Solutions Primarily Probable Solutions
Lack of skills, knowledge, information
training, job aid, education, documentation, performance support tools, knowledge bases, communication initiatives, clear and updated expectations
Lack of motivation
training, and education, participatory goal setting, job aids, documentation, performance support tools, knowledge bases, communication initiatives, selection of individuals who want to do it
Lack of environment, tools, processes
new or improved tools, job design, job enrichment, workplace design, reengineered processes
Lack of incentives
new policies, revised performance management system, management development initiatives Source: Rossett, 2009
As Pershing (2006) indicates, designing performance improvement interventions comprises detailed plans and decisions appearing after some phases, such as performance and cause analyses, specifying characteristics of interventions, and detailing evaluation plans. In contrast, developing process requires practitioners to convert the design specifications into factual interventions and strategies for the desired implementation. More specifically, design and development processes vary in terms of selected interventions’ attributes (Watkins, 2007c). In
5
UBIQUITOUS LEARNING: AN INTERNATIONAL JOURNAL
sum, the designers can reach a conclusion about what primary probable solutions would the best to fit the performers for doing their job well related with the standards and objectives determined in the analysis phase (Molenda and Pershing, 2004).
The U-PI Model As the ubiquitous computing technologies and environment, and intervention selection in the HPT described above began to consolidate, the following U-PI Model (Ubiquitous Performance Improvement Model) emerged. This model encompasses both the key performance drivers and the characteristics of the ubiquitous computing and technologies. By helping the selection of the appropriate u-systems to remedy performance problems, performance technologists using this model might decide on a justifiable set of performance solutions and discover the critical connections between environmental and personal factors associating with ubiquitous technologies, learning concept, and contextual information (Figure 1). Examined in full detail, the model is composed mainly of many concepts and components each with a specific dynamic in itself and in relation to proper elements of the model. The term “u-systems” used throughout the research paper is the abbreviation for components related with specific ubiquitous learning elements, methods and technologies that are used in the U-PI Model.
Selecting U-systems to Alleviate Internal Performance Factors In the U-PI model, it is proposed that u-systems can be selected as instructional and noninstructional performance support intervention to solve internal (personal) performance obstacles in any organization. More specifically, appropriate u-systems can be selected as performance interventions if the root causes of performance in any organization are lacks of performers’ skills, knowledge, and information or motivation. In that case, performance technologists should judge the value of potential solutions and also decide whether instructional or non-instructional interventions integrated with ubiquitous technologies would be designed and implemented. In HPT, instructional performance support interventions might be decided as right for an organization where there is a gap that exists between job specifications with current knowledge and the skill or attitude of performers (Van Tiem et al., 2001, 2004). These interventions featuring ubiquitous computing and technologies in design can also be applied to performance based educational and training sessions in any organization. Using the ubiquitous learning environments, different educational activities can be integrated with job tasks embedded in real life situations to fix deficiencies in the performers’ motivation, skills, knowledge and information. More specifically, ubiquitous technologies may provide performers with appropriate learning materials in real-world contexts (Huang et al, 2011b) to learn new job tasks, tools required for performance, training materials, policies, goals or any information related to their job. Mobile technology might provide many opportunities in a way that stimulates portable and interactive ubiquitous environments assisting not only self-directed and independent learning but also interactivity with other people (Ng et al, 2010). In a ubiquitous environment, users can work with other people leading to local collaboration (Perrotta, 2011). These features integrated in the ubiquitous learning cover communication initiatives offered in the context of HPT as intervention strategies to relieve basic influences of human behaviors that impact performance improvement. In general, these approaches allow organizations to establish learning environments supporting collaboration with learners in project work and problem solving in real life contexts (Ng et al, 2010). With the characteristics of situating of instructional activities mentioned above, learning is integrated to the learners’ daily lives and assignments (Ng et al., 2010). As Hsieh et al (2011) pointed out that sharing of knowledge, peer interaction, collaboration and exchange of experience are other learning activities which may improve personal factors. 6
YAKIN AND GUNAY: SELECTION OF UBIQUITOUS COMPUTING TECHNOLOGIES
Figure 1: The U-PI Model Besides instructional support, the u-systems can also be developed and implemented as noninstructional performance support interventions. In the HPT literature, job aids, electronic
7
UBIQUITOUS LEARNING: AN INTERNATIONAL JOURNAL
performance support systems, documentation and standards have been categorized as noninstructional performance support systems (Van Tiem et al, 2004). In these types of performance support, the tools are selected and implemented for motivating performers and improving processes, products and services and managing organizations’ plans, materials, results and success evaluations (Van Tiem et al, 2004). Just-in time and just-enough information are offered to performers for the purpose of both motivation and performing tasks well via non-instructional performance support systems. Indeed, the ubiquitous computing allows members of an organization to create reports (Loureiro et al, 2007) about any performance related topic and disseminate them to their other colleagues in a ubiquitous way. It is apparent that these efforts enable the organization to create both a performance related knowledge base and valuable documentation including job tasks and processes. Consistent with the context of HPT, the ubiquitous learning is independent from any physical space, plans, or schedule; therefore, it can occur anywhere and anytime (Ng et al, 2010). Consequently, more possibilities and flexibility in job tasks might be provided to the performers via using ubiquitous computing and technologies in non-instructional performance interventions. Using ubiquitous computing technologies, communication initiatives can be established by team leaders or chiefs to share experiences about job tasks and other performance related issues or talk only about leisure facilities. These social communication environments may help so as to increase the performers’ motivation. In doing so, performers have also a chance to set up real time meetings by using podcasts or other technologies whenever needed in order to create and share knowledge, experience and performance issues. To summarize, the u-systems designed as instructional or non-instructional performance support interventions might be used to close the performance gap if there are any performance issues pointing out a lack of motivation and skills, knowledge, and information are identified in the organization.
The Connections between External (Environmental) Performance Drivers and Contextual Information Besides theoretical assumptions mentioned above, the U-PI model suggests some practical implications for practitioners. In the model, the connections grounded on u-systems as performance improvement interventions to solve internal performance issues can serve a unique conceptual premise for both the HPT field and ubiquitous learning. Because fast, seamless and adaptive connections can be established through the ubiquitous environment, users and resources are linked using with connected devices and maintained beyond a range of locations (Perrotta, 2011). The specific characteristics of the HPT and u-systems make these visionary utilizations. In the HPT literature, the whole solution system for performance deficits in any organization requires a number of interventions, not a single package (Van Tiem, 2004). This situation stems from the complexities of performance obstacles. More specifically, after conducting performance and cause analyses, practitioners might offer education, workplace design, and performance management system as a solution package to improve the performance of the organization. Therefore, the whole system should include these elements. In that condition, these interventions can be also linked with each other. The connections between these solution systems should be established in some performance improvement efforts. The u-systems’ roles in these complex situations may be twofold. First, the selected usystems may serve as input, and second they may present valuable information as output. In HPT, any evaluation effort conducted after the implementation phase produces valuable information about the effectiveness of the intervention. Since the information about the performers’ preferences, learning styles or current skills, abilities and knowledge base (user context) and job processes or workspace conditions (physical context) can be integrated in any usystems, these stored and recorded data would provide valuable information to the other elements
8
YAKIN AND GUNAY: SELECTION OF UBIQUITOUS COMPUTING TECHNOLOGIES
of the organization. In general, using the sensor technology, it is possible to record and detect users’ actions and behaviors displayed in the ubiquitous learning environments (Chu et al, 2010; Hung et al, 2014). Just now, the ubiquitous learning systems have been designed to offer technological assistance for gathering and recording learner behaviors or learning events in activities (Huang and Wu, 2011a). The recorded data can be used for reviewing and evaluation (Hsieh et al, 2011). This performance related data obtained through real-world and digital world applications can be also utilized for any performance-based management initiative. For example, the evaluation data obtained from the ubiquitous educational or training activities might be used as input for performance management policies or systems regarding performers’ individual preferences or job and workspace designs. Then, information and knowledge obtained through these efforts can be used as internal or external context in management systems for interpretations of the organizational or personal performance or any future performance improvement effort. Likewise, internal or external contextual information about performers embedded in the other learning or management systems might help performance technologists to use them as output information in other selected mixed of performance interventions. Generally, practitioners can design more suitable learning environments and systems when they obtain more information about the learners’ individual perspectives in learning environments (Tsai, 2005). Because performers’ learning styles and preferences may be pre-determined (Tsai and Hwang, 2012) in usystems, performance related information as output data can be embodied in the designing process of the new interventions. With this contextual information of the performers, personalized supports (instructional or non-instructional) for them can be established for efficient and effective implementations of the performance improvement interventions. It is apparent that these propositions including contextual input and output information might be noteworthy for the complex and justifiable set of performance solutions.
Conclusion In HPT context, an intervention as a process is planned assessments which are designed and developed so as to relieve and sort out performance problems (Van Tiem, 2004). That is why interventions have an effect on the job performance. Namely, specific needs that point out gaps between current and future situations for an organization may be fulfilled with designing and developing interventions (Pershing, 2006). The solution should be both technically and theoretically correct and capable of solving the performance problems (Brinkerhoff, 2006). That is why the selection of performance improvement intervention process is so important for the achievement of the HPT initiatives. In general, both formal and informal activities can be implemented in the ubiquitous learning environments (Hsieh et al, 2011).Ubiquitous computing comprises a world where applications can be integrated in everyday objects (Loureiro et al, 2007). In other words, real-world and digital applications and resources are combined in the ubiquitous learning environment (Chu et al, 2010). The same approach is valid for the organizational environment and processes. As proposed in this paper; therefore, it can be claimed that ubiquitous learning and technologies can be used as performance improvement interventions in any HPT project efforts. In this paper, the U-PI Model is proposed not only to simplify the selection process of the ubiquitous technologies and environments but also to elicit the appropriate conditions to solve performance issues. According to the U-PI model, the solution systems based on the ubiquitous technologies and computing might be appropriate intervention strategies provided that the primary causal factors of the performance issues are related with internal factors. Only instructional and non-instructional interventions or blended solutions integrated in ubiquitous computing or technologies may be remedial if the performance gaps are caused by a lack of skills or knowledge, on the part of the performers in the organizations.
9
UBIQUITOUS LEARNING: AN INTERNATIONAL JOURNAL
To establish a link between the ubiquitous performance interventions and the external systems, the U-PI model proposed a connection schema. In ubiquitous computing, context has been defined in ways with different names. Loureiro et al (2007) stated that while the internal context encompasses providing information about the state of the users, the external context specifies the environment in which the users are involved. Using contextual information, Huang and Wu (2011a) stated that obtaining detailed contextual information about learner situations in materials and activities provide teachers with perceptions regarding their behaviors, knowledge, skills, and characteristics. Indeed, by combining context-awareness functionality with ubiquitous learning environments, learners’ learning conditions can be determined (Shih et al, 2011). To adapt these attributes offered by ubiquitous learning to the HPT context, the connection between u-systems, external performance factors and interventions are visualized in the U-PI model. In doing so, the contextual information which one of the important features of the ubiquitous systems can be used as both input and output for the other intervention packages and future performance improvement initiatives. Recently, ubiquitous computing, technologies and learning have drawn increasing attention from educators, teachers and researchers. With the help of mobile and sensing technologies, ubiquitous learning has enormous potential for motivating learners and increasing their learning performance (Hung et al, 2014). Chen et al (2009) pointed out, however, that teaching and learning activities are not limited only to classrooms and schools. The performance problems appeared and identified in all sizes and types of organizations, including private businesses, government, social service, nonprofit organizations, educational institutions, and the military might be solved via integrating ubiquitous technologies with the concepts of HPT. In the literature, some performance related concepts, approaches and technologies such as EPSS and knowledge management tools have been utilized by using ubiquitous computing and learning. However, there has been little research in this area. Besides instructional performance support interventions such as u-training, u-mentoring and coaches, non-instructional support systems such as job aids, documentation and communication opportunities remain under-researched. As visualized and discussed in the U-PI Model, the researches and practical implementations regarding the use of contextual information whether as input or output in performance improvement initiatives should be also examined through empirical studies. In general, models are important for HPT because of the fact that they form the performance improvement initiatives and provide a framework for scientific and systematic inquiry (Burner, 2010). Besides development of the models, validation and evaluation studies should also be carried out. The main work that is required in the future based on the premises offered in the U-PI Model should be directly measured and evaluated. More specifically, in case of blended or mixed u-system solutions, how the context information can be integrated in the other systems and whether these integrations produce valuable results regarding both the performance of technological devices and theoretical approaches based on the HPT context might be important research areas to judge the model. Because, in consequence of a multi-dimensional nature of the performance problems, quality-improvement initiatives and business opportunities, one intervention might not be sufficient for filling the gaps (Guerra-Lopez, 2013; Jang, 2008; Pershing, 2006; Van Tiem, 2004). Indeed, it is really difficult to appraise finding solutions to solving performance problems (Siko, 2013). Therefore, blended solutions may be also be phased in selecting needed interventions. As long as selected interventions produce intended results for the organizations, no matter which performance technologies and interventions are combined or blended (Watkins, 2007a).To conclude, there still remains an inadequacy of empirical data exploring how contextual information could be used in a justifiable set of blended u-systems.
10
YAKIN AND GUNAY: SELECTION OF UBIQUITOUS COMPUTING TECHNOLOGIES
REFERENCES Addison, R. M., and Haig, C. 2006. “The Performance Architect’s Essential Guide to the Performance Technology Landscape.” In J. A. Pershing (Ed.), The Handbook of Human Performance Technology, 3rd ed.: (35-54). San Francisco: Pfeiffer. Brinkerhoff, R. O. 2006. “Using Evaluation to Measure and Improve the Effectiveness of Human Performance Technology Initiatives.” In J. A. Pershing (Ed.), The Handbook of Human Performance Technology, 3rd ed. (287-311). San Francisco: Pfeiffer. Burner, K. J. 2010. “From Performance Analysis to Training Needs Assessment.” In K. H. Silber, and W. R. Foshay (Ed.), Handbook of Improving Performance in the Workplace, Vol. 1: Instructional Design and Training Delivery (144-183). San Francisco: International Society for Performance Improvement. Chen, C. H., Hwang, G. J., Yang, T. C., Chen, S. H., and Huang, S. Y. 2009. “Analysis of a Ubiquitous Performance Support System for Teachers.” Innovations in Education and Teaching International, 46(4): 421-433. Chyung, S. Y. 2008. Foundations of Instructional Performance Technology. Amherst, MA: HRD Press Inc. Chu, H. C., Hwang, G. J., and Tsai, C. C. 2010. “A Knowledge Engineering Approach to Developing Mindtools for Context-Aware Ubiquitous Learning.” Computers & Education, 54: 289-297. Desautels, B. 2006. “The Impact of Organizational Development.” In J. A. Pershing (Ed.), The Handbook of Human Performance Technology, 3rd ed.: 571-591. San Francisco: Pfeiffer. El-Bishouty, M. M., Ogata, H., Rahman, S., and Yano, Y. 2010. “Social Knowledge Awareness Map for Computer Supported Ubiquitous Learning Environment.” Educational Technology & Society, 13 (4): 27–37. Elliott, P. 1998. “Assessment Phase: Building Models and Defining Gaps.” In D. G. Robinson and J. C. Robinson (Eds.), Moving from Training to Performance: A Practical Guidebook (63-77). San Francisco: American Society for Training & Development (ASTD) and Berrett-Koehler. Hale, J. 2007. The Performance Consultant’s Fieldbook: Tools and Techniques for Improving Organizations and People, 2nd ed. San Francisco: Pfeiffer. Hotek, D. R., and White, M. R. 1999. “An Overview of Performance Technology.” The Journal of Technology Studies, 25(3): 43-50. Hsieh, S. W., Jang, Y. R., Hwang, G. J., and Chen, N. S. 2011. “Effects of Teaching and Learning Styles on Students’ Reflection Levels for Ubiquitous Learning.” Computers & Education, 57: 1194-1201. Hung, I-C., Yang, X-J., Fang, W-C., Hwang, G-J., and Chen, N-S. 2014. “A Context-Aware Video Prompt Approach to Improving Students’ In-field Reflection Levels.” Computers & Education, 70: 80-91. Huang, Y. M., and Wu, T. T. 2011a. “A Systematic Approach for Learner Group Composition Utilizing U-learning Portfolio.” Educational Technology & Society, 14(3): 102-117. Huang, Y. M., Chiu, P. S., Liu, T. C., and Chen, T. S. 2011b. “The Design and Implementation of a Meaningful Learning-based Evaluation Method for Ubiquitous Learning.” Computers & Education, 57: 2291-2302. Hung, I. C., Yang, X. X., Fang, W. C., Hwang, G. J., and Chen, N. S. 2014. “A Context-aware Video Prompt Approach to Improving Students’ In-field Reflection Levels.” Computers & Education, 70: 80-91. Hwang, G. J., Tsai, C. C., and Yang, S. J. H. 2008. “Criteria, Strategies and Research Issues of Context-aware Ubiquitous Learning.” Educational Technology & Society, 11(2), 81-91.
11
UBIQUITOUS LEARNING: AN INTERNATIONAL JOURNAL
Hwang, G. J., Yang, T. C., Tsai, C. C., and Yang, J. H. S. 2009. “A context-aware Ubiquitous Learning Environment for Conducting Complex Experimental Procedures.” Computers & Education, 53(2): 402–413. Jang, H. Y. 2008. “Reconsidering Human Performance Technology.” Performance Improvement, 47(6): 25-33. Guerra-Lopez, I. 2013. “Performance Indicator Maps: A visual tool for Understanding, Managing, and Continuously Improving Your Business Metrics.” Performance Improvement, 52(6): 11-17. Loureiro, E., Ferreira, G., Almeida, H., and Perkusich, A. 2001. “Pervasive Computing: What is It Anyway?” In M.D. Lytras, and A. Naeve (Eds.), Ubiquitous and Pervasive Knowledge and Learning Management: Semantics, Social Networking and New Media to Their Full Potential (1-34). Hershey: Idea Group Publishing. Miles, D. H. 2003. The 30-Second Encyclopedia of Learning and Performance: A Trainer's Guide to Theory, Terminology, and Practice. New York: American Management Association. Molenda, M., and Pershing, J. A. 2004. “The Strategic Impact Model: An Integrative Approach to Performance Improvement and Instructional Systems Design.” TechTrends, 48(2): 26-32. Ng, W., Nicholas, H., Loke, S., and Torabi, T. 2010. “Designing Effective Pedagogical Systems for Teaching and Learning with Mobile and Ubiquitous Devices.” In T. T. Goh (Eds.), Multiplatform e-Learning Systems and Technologies: Mobile Devices for Ubiquitous ICT-Based Education (42-56). Hershey: Information Science Reference Perrotta, C. 2011. “Ubiquitous learning vs. the value of boundaries: Reflections on five years of ‘Innovation in Education’”. In C.D. Kloos, D. Gilet, R.M.C. Garcia, F. Wild, and M. Wolpers (Eds.). Towards Ubiquitous Learning, 6th European Conference on Technology Enhanced Learning, EC-TEL 2011. (pp. 9-14). Polermo, Italy: Springer. doi: 10.1007/978-3-642-23985-4 Pershing, J. L. 2006. Human performance technology fundamentals. In J. A. Pershing (Ed.), The handbook of human performance technology (3rd ed.) (pp. 5-34). San Francisco: Pfeiffer. Restrepo, C. M. Z., Pulido, J. G. L., Nunez, R. A., Perez, G. P. T., and Meja, C. V. 2013. “TAG: Introduction to a Ubiquitous Learning Model to Assess the Ubiquity Level in Higher Education Institutions”. Ubiqutious Learning: An International Journal, 5(2): 1-17. Rosenberg, M. J. 1990. “Performance Technology: Working the System.” Training, 27(2): 4248. Rossett, A. 2009. First Things Fast: A Handbook for Performance Analysis, 2nd ed. San Francisco: Pfeiffer. Shih, J. L., Chu, H. C., Hwang, G. J., and Kinshuk. 2011. “An Investigation of Attitudes of Students and Teachers about Participating in a Context-aware Ubiquitous Learning Activity.” British Journal of Educational Technology, 42(3): 373-394. Siko, J. P. 2013. “Using the I2 Intervention Matrix to Select the Best Course of Action.” Performance Improvement, 52(10): 23-26. Stolovitch, H. D. 2000. “Human Performance Technology: Research and Theory to Practice.” Performance Improvement, 39(4): 7-16. Stolovitch, H. D. 2007. “The Development and Evolution of Human Performance Improvement.” In R. A. Reiser & J. V. Dempsey (Eds.), Trends and Issues in Instructional Design and Technology (134-146). Upper Saddle River, NJ: Merrill Education/Prentice-Hall. Stolovitch, H., and Keeps, E. 1998. “Implementation Phase: Performance Improvement Interventions.” In D. G. Robinson, and J. C. Robinson (Ed.), Moving from Training to Performance: A Practical Guidebook (95-134). San Francisco: American Society for Training & Development (ASTD) and Berrett-Koehler.
12
