Situational Language Teaching in Ubiquitous Learning Environments

Abstract

Situational language teaching (SLT) is an effective instruction paradigm for English teaching in terms of providing vocabularies and sentence patterns with their frequent situations through learning materials. With the growth of educational technology, we need powerful and suitable techniques to embody SLT’s features in ubiquitous learning (u-learning) and thus to benefit teachers and learners. Although researchers have proposed several innovative types of u-learning scenarios, the improved SLT paradigm in u-learning environment has been rarely investigated. This study indicated a framework of ubiquitous learning school to promote the concept of u-learning and employ SLT pedagogy in u-learning environment; it is called U-SLT. In order to support its innovation and provide situational learning services on demand, situational mashups was suggested to identify learners’ situation and learning requirement by means of integrating situation awareness with service mashups. The comparison between two u-learning modes, learning with situational mashups and learning without situation awareness support, were discussed. Experimental results showed that the students with the situational mashups support had a better learning performance and improved behaviours. Therefore, situational mashups was perceived to be a useful and desirable system for supporting U-SLT as well as the fundamental issue of a ubiquitous learning school.

Full text

Knowledge Management & E-Learning: An International Journal, Vol.2, No.3. 312
Situational Language Teaching in Ubiquitous Learning
Environments
Angus F.M. Huang
Institute of Information Science, Academia Sinica, 128 Academia Road,
Section 2, Nankang, Taipei 115, Taiwan.
E-mail: angusfuminghuang@gmail.com
Stephen J.H. Yang*
Department of Computer Science and Information Engineering,
National Central University, No.300, Jhongda Rd., Jhongli City,
Taoyuan County 32001, Taiwan
E-mail: jhyang@csie.ncu.edu.tw
Gwo-Jen Hwang
Graduate Institute of Digital Learning and Education, National Taiwan
University of Science and Technology, 43, Sec.4, Keelung Rd., Taipei,
106, Taiwan
E-mail: gjhwang.academic@gmail.com
*Corresponding author
Abstract: Situational language teaching (SLT) is an effective instruction
paradigm for English teaching in terms of providing vocabularies and sentence
patterns with their frequent situations through learning materials. With the
growth of educational technology, we need powerful and suitable techniques to
embody SLT’s features in ubiquitous learning (u -learning) and thus to benefit
teachers and learners. Although researchers have proposed several innovative
types of u-learning scenarios, the improved SLT paradigm in u-learning
environment has been rarely investigated. This study indicated a framework of
ubiquitous learning school to promote the concept of u-learning and employ
SLT pedagogy in u-learning environment; it is called U-SLT. In order to
support its innovation and provide situational learning services on demand,
situational mashups was suggested to identify learners’ situation and learning
requirement by means of integrating situation awareness with service mashups.
The comparison between two u-learning modes, learning with situational
mashups and learning without situation awareness support, were discussed.
Experimental results showed that the students with the situational mashups
support had a better learning performance and improved behaviours. Therefore,
situational mashups was perceived to be a useful and desirable system for
supporting U-SLT as well as the fundamental issue of a ubiquitous learning
school.
Keywords: Ubiquitous Learning, Situational Language Teaching, Mashups,
Situation Awareness, Ubiquitous Situational Language Teaching.

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Biographical notes: Dr. Angus F.M. Huang received the B.S. degree in the
department of information management from the Yuan Ze University in 2003,
and the M.S. degree in network learning technology from the National Central
University in 2005, both in Jungli, Taiwan. He received the Ph.D. degree in
computer science and information engineering from the National Central
University. He is a postdoctoral researcher in the Institute of Information
Science, Academia Sinica, Taiwan. His current research interests concerns
software engineering, knowledge engineering, instructional technology,
information management, pervasive computing, and prediction market.
Dr. Stephen J.H. Yang is a Distinguished Professor of Computer Science &
Information Engineering, and the Associate Dean of Academic Affairs at the
National Central University, Taiwan. Dr. Yang received his Ph.D. degree in
Electrical Engineering & Computer Science from the University of Illinois at
Chicago in 1995. His research interests include knowledge management,
Second Life, mobile learning, Web 2.0, semantic Web, social networks, context
aware & ubiquitous learning.
Gwo-Jen Hwang is currently a Chair Professor in the Graduate Institute of
Digital Learning and Education at National Taiwan University of Science and
Technology. In 1991, Dr. Hwang received his Ph.D. degree in Computer
Science and Information Engineering from National Chiao Tung University in
Taiwan. His research interests include mobile and ubiquitous learning,
computer-assisted testing, expert systems and knowledge engineering. Dr.
Hwang has published more than 330 academic papers, including 118 papers in
such professional journals as Computers & Education, Educational Technology
& Society, British Journal of Educational Technology, Innovations in
Education and Teaching International, and The Electronic Libraries among
others. Owing to the good reputation in academic research and innovative
inventions of e-learning, in 2007, he received the annual Most Outstanding
Researcher Award from the National Science Council in Taiwan.
1. Introduction
Because of the growth of educational technology, many studies were devoted to
improving instruction paradigm with advanced devices or various learning processes.
Situational language teaching (SLT) is a term not commonly used today, but it is an
approach developed by British applied linguists in the 1930s to the 1960s (Richards &
Rogers, 1986). The instruction paradigm impacts language courses and teaching activities
by its continuous use today. The structural view of a language is the basis for the oral
approach and SLT. Speech as the basis of language and structure is regarded as the heart
of the speaking ability (SIL, 1999). The instruction paradigm emphasizes that language
structures must be presented in situations in which they could be used, implying its
distinctiveness to SLT. Among the SLT activities, lecturers teach English vocabularies
and sentence patterns in frequent situations through books, learning materials, photos,
body language, fictitious scenarios. Teachers do not explain grammars and interpret
sentences directly. Students must understand situations and apply the language and
implied meaning to practice the related learning activities, e.g. repetition, written
examination, dictation, oral conversation, and extended reading.
Beyond desktop learning, knowledge sources and learning experiences exist
everywhere. Many effective e-learning materials not only appear on desktop but rise in

314 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
ubiquitous environments, e.g. museum resources, nature observations, distance education,
social contacts, medium transmission and personal experience. Many studies utilized
context-aware techniques to identify learners’ environments and requirements or to assist
learners in acquiring information from the physical world (Silius, Miilumäki, Huhtamäki,
Tebest, Meriläinen & Pohjolainen, 2010; Yang 2006; Hwang, Tsai & Yang, 2008). This
paradigm of getting learning services anywhere anytime is called ubiquitous learning (u-
learning). These contributions promote various human-environment interactions and
enhance learners’ knowledge cognition from the real world (Graf, Yang, Liu & Kinshuk,
2009).
Ubiquitous Learning School
Digital Learning
Service Context Awareness
Technology Seamless Circulation
Design
Lifelong Instruction Adaptive Instruction Situational Instruction
Figure 1. Framework of a Ubiquitous Learning School
The needs of u-learning must take the functional and cognitive requirements into
account from learners and teachers’ perspectives. Therefore, context awareness or
situation awareness is one kernel concept to achieve the u-learning process, such as
environment information, learner capability, learning goals and so on (Vreman & Jong
2006; Teo & Chai, 2009). To clearly popularize u-learning in school even in daily life,
the authors suggested a framework of a ubiquitous learning school as shown in Figure 1.
Digital learning service, context awareness technology, and seamless circulation design
will construct the foundation of a ubiquitous learning school. Meanwhile, the u-learning
school supports the lifelong instruction, adaptive instruction, and situational instruction.
From the SLT viewpoint, programs such as news, soap operas and documentaries have
the potential to enhance the learners’ language experience by showing the target language,
culture and context of use (Fallahkhair, Pemberton, & Griffiths, 2007). However, SLT
does not smoothly achieve relevant learning activities in the ubiquitous learning school
for the digital learning services and human-environment computing paradigm.
Accordingly, this paper proposes the Ubiquitous SLT and achieves it by situational
mashups.
Based on the vision of a u-learning school and the concept of situational language
teaching, the authors define a new SLT paradigm in the u-learning environment.
Ubiquitous SLT (U-SLT) aims at authoring teachers’ SLT-based learning activities in a
digital learning form and to publish them on the Internet or deploy them on the

Knowledge Management & E-Learning: An International Journal, Vol.2, No.3. 315
ubiquitous computing environment. Along with the teaching support, students can obtain
situational learning according to teachers’ SLT-based pedagogy. Based on the scenario,
students can understand the meaning and knowledge of the learning goal from multiple
learning resources. U-SLT learners are people who use ubiquitous learning services on
portable computing devices to proceed with SLT learning activities. They need a uniform
and convenient platform to receive situational learning services, e.g. course slides, video
conference, on-line dictionary, instant messaging, game-based learning guidance, and so
on. The goal of U-SLT is to achieve SLT pedagogy in a u-learning environment that
accounts for student individuality and learning situations.
With the evolvement of software engineering, ubiquitous computing and Web 2.0,
the support of end user programming becomes more desirable. In addition to the silver
bullet of heterogeneous systems integration solution, Web services, Web 2.0 initiated the
civilian programming environment (Yang, Okamoto & Tseng, 2008). The rising
popularity of user-driven web applications, including Blogs, Wikis, Mashups, RSS,
Podcast, Social Networks, P2P networking and Communities, has reflected the aspired
initiative of users. Enterprises utilize relevant technologies in Web 2.0 not only for
sharing daily thought, but for also for communicating with business partners, improving
customer service, integrating suppliers, and managing knowledge internally. Most
importantly, mashups provide a universal solution for users with a non-IT background to
program specific applications. The Mashups component is a widget. Widget can be
operated with a pure program or invoke other Web services. Some popular Mashups
platforms work on the Internet, such as iGoogle (http://www.google.com/ig), MyYahoo
(http://my.yahoo.com), Hinet Xuite (http://www.xuite.net), Yahoo! Pipes
(http://pipes.yahoo.com), Microsoft Popfly (http://www.popfly.ms), and Windows Live
(http://www.live.com) as shown in Figure 2. Although all of these tools provide some
kind of support for end user programming, none of them focuses on context awareness
and their design does not support situational mashups. Automatically composing
necessary situational mashups is important, when the end user roams in a ubiquitous
computing environment and executes different tasks, such as ubiquitous learning and
real-time business. The authors believe that the mobility and flexibility of the mashups
technique can assist in executing situational instruction.
Situational mashups has been addressed in other studies. For example, QEDWiki
is a wiki-based mashups software published by IBM (QEDWiki, 2007). However, to the
best of our knowledge there are currently no environments that are designed specifically
to support context awareness and automatic composition in situational mashups. In order
to better achieve context awareness and facilitate situational mashups the authors firstly
developed a context-aware SOA-based mashups system based on researching into
mashups in Web 2.0, context awareness, end user programming by applying their vision
on situational mashups activities.
The general purpose of this study was to develop a situational mashups system
and achieve the pedagogy of situational language teaching in a u-learning environment.
In this study the authors’ main concern was how context-aware mechanism supports
automatic situational mashups and how this mechanism can best be improved. To
formularize this study, the design problems are defined as follows:
1. How should a context awareness mechanism be developed to facilitate situational
mashups?
2. How to describe students’ situation and the related context information?
3. How to realize the students’ situation and learning requirements?

316 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
4. How to offer the required situational learning services?
5. How to choose the optimal services composition based on the multiple context
constraints?
6. How does such a situational mashups support situational language teaching in a
u-learning environment?
Figure 2. Four popular mashups platforms in Internet, they are iGoogel, MyYahoo,
Hinet Xuite, and Windows Live.
The authors categorize the primary characteristics of SituMash as follows: (1)
Situation awareness- it can interpret student’s context factors and reason students’
specific situation. (2) Automatic composition- it can compose student’s necessary
mashups with its planning. (3) Service orientation- it uses service as the software basis
that students are need not to build the kernel components. (4) End-user autonomy-it
allows students to set the configuration of mashups with their favorites or habits. (5)
Single platform- it proposes a universal platform to avoid conflicts between
heterogeneous systems and to provide an integrated using experience. Finally, the
implementation of the SituMash system is discussed in detail, followed by results derived
from applications and evaluation, which the situational mashups has performed
successfully.
In this paper the authors concentrate on the design and development of a
situational mashups system for ubiquitous situational language teaching in a ubiquitous
learning school. The system aims to employ the strengths of situation awareness and
service mashups not only to identify the learners' situation and learning requirements but
also to provide situational and recommended learning services. Ubiquitous SLT that takes
advantage of situation awareness and service mashups is the primary focus of the
Situational Mashups. The experiments showed that the situational mashups was
perceived to be a useful and desirable system to support ubiquitous situational language
teaching and also the fundamental issue of a Ubiquitous Learning School.
This paper is organized as follows. Firstly, the authors propose the research
questions and observe related literature. Then, the system design and research

Knowledge Management & E-Learning: An International Journal, Vol.2, No.3. 317
methodology is introduced. After the implementation, the system efficiency is evaluated.
Finally, the authors conclude the work and suggest further steps.
2. Literature Review
The concepts of situational computing, ubiquitous learning, and Web 2.0 and mashups
trigger the improvement of ubiquitous situational language teaching that this paper
proposes. This section discusses the related developments and the literature review.
2.1 Situational Computing
Situational Computing is an advanced concept in assisting people better proceed with
daily work and problem solving. Situations use a series of participant, activity, time,
location, equipment to express user’s specific state. The situational computing evaluates
one user’s state and provides him or her with the necessary services. There are several
researches devoted to the study of situation detection so far. The core technology is
Context Awareness (Dey, Salber & Abowd, 2001). Context awareness is used to detect,
represent, and inference the state and behavior of users. Up to now, it has focused on
various studies pointing out the academic and industrial applications for a human-
computer interaction environment. Context awareness supports several applied sciences,
such as health-care system, recommendation system, sensor network, smart environment,
and life long learning. Building situational system is a complex, dynamic and iterative
process, especially for a busy businessman or a mobile user facing various activities in a
running trigger of events. Manifold context awareness mechanisms are achieved for users.
A complete system development tool kit for building context-aware applications (Loke,
2006) and the mechanism of analyzing the uncertainty of context information
(Ranganathan, Al-Muhtadi, & Campbell, 2004) were proposed. However, these studies
do not provide support for end user programming towards the opportunity for an
autonomous service selection.
2.2 Ubiquitous Learning
Microsoft Research proposed a new class of applications that relies on real-time sensor
data and its mash-up with the geocentric Web to provide instantaneous environmental
visibility and timely decision supports. They challenged the data publishing, scalable data
management, data visualization and sensor discovery issues to make users can take
advantage of the portal and tools to make queries over live data sources. In order to
extend the potential of SensorMap, they also provide the mashup APIs. Users can mash
up the online live data sources with other applications. It is powerful. Nevertheless, it is
not easy for end-users to compose these applications. A context-interactive application
(Tan, Liu, & Chang, 2007), based on radio frequency identification (RFID), Internet,
ubiquitous computing, and embedded system, has been developed. This system provides
a mobile-based interactive learning environment (MOBILE) server for teachers and
mobile-tools (m-Tools) for students. Teachers can achieve their outdoor teaching
pedagogy effectively by providing the context-aware expert guidance and outdoor
learning tools. Students can observe outdoor conditions and materials attentively; further,
get the useful knowledge with these technologies.
Microsoft Research proposed a new class of applications that relies on real-time
sensor data and its mash-up with the geocentric Web to provide instantaneous
environmental visibility and timely decision supports. They challenged data publishing,
scalable data management, data visualization and sensor discovery issues to make users
take advantage of the portal and tools to query over live data sources. In order to extend

318 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
the potential of SensorMap, they also provide the mashup Application Programming
Interface. Users can powerfully mash up the online live data sources with other
applications. Nevertheless, it is not easy for end-users to compose these applications. A
context-interactive application (Tan, Liu, & Chang, 2007), based on radio frequency
identification (RFID), Internet, ubiquitous computing, and embedded system, has been
developed. This system provides a mobile-based interactive learning environment
(MOBILE), server for teachers and mobile-tools (m-Tools) for students. Teachers can
achieve their outdoor teaching pedagogy effectively by providing the context-aware
expert guidance and outdoor learning tools. Students can observe outdoor conditions and
materials attentively; further, students have access to useful knowledge by the use of
these technologies.
2.3 Web 2.0 and Mashups
A comprehensive survey of Web 1.0 applications results in the fact that they are mainly
database driven. Developers build an application logic to manipulate the databases. From
the original database schema, web surfers can obtain rich information. However, these
applications are centralized and rely on their own relational database, limiting the
possibilities for data integration. Mashups is a rapid growing Web development paradigm
and provides rich user experience. The mechanism can assist in combining different Web
applications through supported technologies, such as Ajax, SOAP, REST, screen scraping,
Semantic Web, RDF in a succinct program development way. Duane Merrill offers a
brief survey of the prominent mashup genres (Merrill, 2006). These are mapping
mashups, video and photo mashups, searching and shopping mashups and news mashups
at present. For example, one of the big catalysts for the appearance of mashups was the
Google’s project, called Google Maps API. Another popular application is the news
mashups. Syndication feed mashups (Lindahl & Blount, 2003) can aggregate user’s feeds
and present them over the Web, creating a personalized newspaper that plays according
to user’s interests or domain. MashMaker assists non-expert users in creating their own
mashups easily based on data and queries proposed by other users and by remote sites
(Ennals & Garofalakis, 2007). They encourage users to find information by exploring,
rather than by writing queries. Users can share data, widgets, and widget
recommendation collaboratively especially. Although they apply the social network to
improve the mashups selection, users have to explore those candidates by themselves
eventually.
3. The Situational Mashups System
The SituMash system supports the situational mashups composition according to the
students’ context and teacher’s teaching goal. The situational mashups environment is
illustrated in Figure 3. There are five parts in such environment, student, computing
device, SOA, mash hub and physical environment. Students will handle some computing
devices and roam at different deployed by service providers. These learning services also
can be invoked by the widgets; therefore, a more convenient software paradigm is
achieved. Students also can arrange the mashups according to their requirements. In order
to achieve more powerful applications and reduce the interruption to students, the authors
propose the SituMash system plugging in the mash hub. The SituMash system can
compose widgets according to student’s situation automatically. Students can move to
different locations and thereby get the specific learning service for U-SLT activities (as
shown in Figure 4). The specific context constraints can lead to students’ specific
situation and the situation will then trigger the system to recompose the necessary
widgets for the students.

Knowledge Management & E-Learning: An International Journal, Vol.2, No.3. 319
Students
SOA
Physical
Environment
Computing
Devices
Mash Hub
at at
deploy deploy
wrap
support
situation
service widget
mashboard
context
Figure 3. Situational mashups environment.
Figure 4. An example of Situational mashups that composes different learning
services in different contexts such as device, location, and task)
3.1. Identifying Requirements
In order to provide students with the situational services when they roam in different
situations, the authors designated the following basic concerns that should be provided by
the SituMash system: (1) When to detect the student’s context? (2) How to reason the
student’s situation? (3) How to determine the necessary widgets for the specific situation?
(4) How to select the optimal widgets from numerous candidate widgets? (5) How to
compose the selected widgets dynamically?
The situation reasoning includes many inference rules. Considering the
complexity of these rules, the students may not be aware of them when the system
reasons the students’ situations. The SituMash system should infer the specific situational
widgets by the students’ context model quietly. One approach could be to design the
system to determine the necessary widgets autonomously and compose them
automatically for students’ uses. The disadvantage of this approach is that students might
have their own opinions and disagree with the recommendation. Another approach could
be to allow the students to modify the widgets. The authors anticipate that the situation

320 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
reasoning mechanism will reason the students’ situation appropriately and the service
inference mechanism will infer the necessary widgets suitable for the situation.
The SituMash consists of the following components: Context Modeling to build a
knowledge model to represent the student’s context factors. Situation Reasoning to
reason the student’s situation through the context model with the situation inference rules.
Service Inference to infer the situational widgets through the student’s situation with the
service inference rules. Mashups Optimization to compose the final widgets in an optimal
way towards the minimal costs and the maximal benefit.
3.2. Context Representation
In order to provide situational mashups for designing support in a ubiquitous computing
environment the authors go deep into the study of context representation. A common
definition of context points towards a five tuple {people, activity, time, location, object}.
However the authors argued that the definition is too vague and widespread. The
characteristics of such model may let it be driven to futility. Therefore, the refined
context definition addresses a Context Factor {Social, Event, Schedule, Location,
Equipment} to reflect the usage of a mobile and ubiquitous computing environment. This
model appears clearer and without a hitch to represent the students’ context. And it will
make the situation reasoning mechanism go smoothly in reasoning the students’ situation
from a context factor. The social factor addresses the people interacting with students; it
will assist in reasoning the students’ situation through the interactive people’s
professional title. The event factor refers to the activity executed by the student. The
schedule means the students’ personal schedule; it will assist in understanding the
students’ assignments in a specific duration. The location factor is a popular context
factor in this research area; it can indicate the students’ working state through a
corresponding location information. The equipment factor addresses the devices
surrounding the students; it may limit and shape the usage of information acquisition.
This context model can be represented in a formal description (Yang & Chen, 2008).
Subsequently, the formal context information can be used to reason the situation in the
following step.
3.3. Situation Reasoning
Advanced research about service provision can obtain student profiles to promote the
satisfaction of the users experience. Nevertheless, changing and dynamic student
situation were not considered by now. They can not infer and predict the student’s
specific situation, therefore they cannot propose applicable services to students. When
using context awareness, the goal of the SituMash system is to create a seamless and
intelligent Web experience for the students by composing necessary widgets with a
workflow planning rather than selecting the widgets manually. Based on previous
research result (Yang, Zhang & Chen, 2008), this study used the rule-based system called
JESS to construct a situation reasoning mechanism. A context interpretation rule base has
been proposed. According to the knowledge of context interpretation, the system can
predict students’ situations such as to attend a meeting, to attend a class, to operate a
vehicle, to join outdoor learning, to visit a museum, and so on. Therefore, the SituMash
system can use this situation information to plan the suitable and necessary tasks towards
the students’ goals. The automatic mechanism also can avoid too many manual students’
inputs by using extra situation information. It is important to rely on the users using
experience in a ubiquitous environment.

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3.4. Workflow Planning
Situational-mashups systems for ubiquitous-computing environments are an emerging
trend in service science. But there is a problem due to the changeable states of the
situations in ubiquitous computing environments. From the characteristics user mobility,
device variety, time validity, location dependence, students require special solutions that
address different mashups guidelines. The set of potentially available mashups widgets
varies because of the above-mentioned characteristics, so it cannot be forecasted and pre-
planed. Situational Mashups should be designed to provide students with the opportunity
of minimum manual inputs of context to achieve the situational tasks in an automatic
widgets composition. The authors consider the widgets configuration as a workflow
planning (Thompson, Li, and Xiao, 2007). The user request is a set of inputs, the user
context is a constraint, and the user’s goal is a set of desired outputs. The widgets are
similarly treated as operators. Thus, planning skills can be used, and a special search that
uses local knowledge of action operators and constraints to find a set of widgets that will
satisfy the inputs (user request) and outputs (desired goal). In the SituMash system, the
Partial Order Planning (POP) algorithm contributes to automatically finding the
necessary widgets.
3.5. Mashups Optimization
It is possible to receive many functional-matching widgets as a result of the workflow
planning mechanism in the widgets repository. Selecting one optimum combination from
the tasks set is an important and difficult issue. From the software point of view, each
widget has its own characteristics, such as cost, response time, and reliability. From the
hardware point of view, each each widget has its own point of view, such as device
computing capability, memory, operating style, and mobility. Finally, from the learning
methodology point of view, authorization, learning age, and course dependency are
important characteristics. In addition, software engineering and ubiquitous computing
concerns play also a role. To optimize the widgets composition according to the
limitation of software and hardware capabilities and students’ requirements is the goal of
the mashups optimization mechanism. This classical software requirement optimization
also has been investigated for a long time. There have been various optimization
solutions proposed, such as mathematical programming, genetic algorithm, ant colony
optimization, cultural algorithm, and particle swarm optimization. In search-based
software engineering (SBSE) research area (Harman & Jones, 2001), there are many
contributions to combinational optimization based on the meta-heuristic mechanisms.
The PSO algorithm is a meta-heuristic methodology, which was designed from the
natural observation of birds seeking for food. This algorithm requires only original
mathematical operators and profits from the need for less computation memory and quick
computing speed (Kennedy & Eberhart, 1995). The sharing of information among
participants offers an evolutionary advantage. This insight was essential for the particle
swarm optimization. This study is based on the PSO algorithm to compute and customize
the optimal mashups composition through the parameters and fitness function designated
by students. Finally, students will obtain the situational and optimal mashups for their
goals in the mashboard starting from the situation reasoning to Efficiency is workflow
planning to mashups optimization.

322 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
4. Evaluation and Discussion
To evaluate the effectiveness of this study, the students’ perceived satisfaction, the
teachers’ perceived satisfaction, and the students’ learning outcome are discussed in this
section.
4.1. Evaluation of the Students’ Perceived Satisfaction
The authors made a series of experiments with the SituMash system to demonstrate the
satisfaction with this system and to explore some phenomena of situational computing.
Students in both groups with the SituMash system (with situational computing) and with
the QEDWiki system (without situational computing) were asked about their perceptions
of the situational language learning activities. Students were asked to fill in a
questionnaire (Table 1) by checking the answers on a Likert-type 5-point scale. 320
students completed the questionnaire, among which, 160 students belonged to the
SituMash group and 160 students belonged to the QEDWiki group. This study compares
the students’ attitudes between the two groups with t-test statistics. The results are shown
in Table 2.
In this experiment, the SituMash group students showed more positive
perceptions about the situational language learning activities than the QEDWiki group
students (42.50% SA vs. 26.88% SA, 28.13% A vs. 26.25% A, Table 2). Also more
QEDWiki group students disliked the learning activities (11.25% D vs. 21.88% D, 4.38%
SD vs. 7.50% SD, Table 2). The t-test results revealed that there were significant
differences in the students’ attitudes between the two groups. The SituMash group liked
situational language learning better (Question 1, t=13.22, p<0.05). Positively, more
SituMash students felt the SLT learning activities are helpful for language learning
(Question 2, t=14.76, p<0.05). The situational learning services composition assists
students in understanding the learning objectives constructively (Question 3, t=10.42,
p<0.05). In addition, the interaction with learning services in SituMash group is better
than QEDWiki (Question 4, t=17.04, p<0.05). However, no significant differences were
found between the two groups regarding the interactions with classmates and teacher, and
the interaction efficiency are bad both (Question 5, t=6.67, p=0.1545, Mean=2.9625 and
2.6875). The authors conjectured that the social interaction is influenced by the design of
the learning services and the personality of the students. Furthermore, it was observed
that the situation reasoning, workflow planning, and mashups optimization can reduce the
loading of the students’ manual operation. Therefore, the results also showed that the
SituMash group indicated that they had better interactions with the platform (Question 6,
t=16.81, p<0.05, Mean=3.9 vs. 3.5).
The authors found an interesting phenomenon thanks to the evaluation of the
interviews. Students answers convinced that the SituMash system can effectively
economize the configuration time, because they do not need to select the widgets
manually. The system can automatically arrange the suitable composition of mashups for
the specific situation that allows them to proceed with the tasks seamlessly. Even the
students felt that the tasks planning mechanism can redeem the ignorance that what tools
they should work on classes. Although the supply of software is rich, students informed
that if the test can cope with other devices they would have felt more convenient, such as
PDA, smart phone, widget player, or thinner Tablet PC. Moreover, students indicated that
the user experience of the pure QEDWiki system refers to inconvenience and troubles.
Summarizing the results, the situation reasoning and the tasks planning mechanisms can
advance the situational and pervasive computing domain.

Knowledge Management & E-Learning: An International Journal, Vol.2, No.3. 323
Table 1. Questions about the students’ perceived satisfaction
Questions
Question Sentences
1
I like U-SLT learning activities.
2
The U-SLT learning activities are helpful.
3
I feel easy to understand each learning objective.
4
Interact with learning services is very easy.
5
I had good interactions with classmates and teacher.
6
I had good interactions with the platform.
Table 2. The students’ perceived satisfaction of U-SLT
Q
Group
SA(%)
A(%)
N(%)
D(%)
SD(%)
Mean
t
p
1
SituMash
QEDWiki
42.50
26.88
28.13
26.25
13.75
17.50
11.25
21.88
4.38
7.50
3.9313
3.4313
13.22
0.0102
2
SituMash
QEDWiki
51.88
36.25
28.13
26.25
15.00
23.13
3.75
9.38
1.25
5.00
4.2563
3.7938
14.76
0,0052
3
SituMash
QEDWiki
26.88
20.00
47.50
37.50
15.00
23.13
7.50
14.38
3.13
5.00
3.8750
3.5313
10.42
0.0340
4
SituMash
QEDWiki
33.13
20.00
45.00
37.50
15.00
30.63
4.38
8.13
2.50
3.75
4.0188
3.6188
17.04
0.0019
5
SituMash
QEDWiki
15.00
8.13
21.25
15.63
23.13
30.00
26.25
29.38
14.38
16.88
2.9625
2.6875
6.67
0.1545
6
SituMash
QEDWiki
26.25
15.63
50.63
48.13
15.00
14.38
3.13
14.38
5.00
7.50
3.9000
3.5000
16.81
0.0021
Note. SA: strongly agree; A: agree; N: neutral; D: disagree; SD: strongly disagree.
*p<0.05
4.2. Evaluation of the Teachers’ Perceived Satisfaction
To demonstrate whether the SituMash system can satisfy the original intention of
situational language learning, this study also asked six teachers to give their perceptions
of using the system and its functions in the seven questions shown in Table 3. 83.33% of
the teachers agreed that the user interface of the SituMash system was clear and
straightforward (Question 1). 83.33% reflected that the learning services appearance in
U-SLT can keep the original intention of traditional SLT (Question 2). Positively, a
majority of teachers agreed that the context representation, the situation reasoning, and
the workflow planning are important, and precise and helpful for SLT learning activities
(Question 3, 50%A; Question 4, 33.33% SA; Question 5, 50% SA). However, teachers
doubted that mashups optimization can affect learning services composition effectively.
66.67% teachers disagreed its effectiveness. Overall, teachers are not confused with the
use of the SituMash system (Question 6, 33.33% SA, 50% A, 16.67% N).

324 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
This result showed a positive evaluation of the SituMash system for assisting
teachers in executing situational language teaching. According to the teachers’ profession
in the SLT pedagogy, the survey revealed that the ubiquitous situational language
teaching can be achieved successfully in a u-learning environment by the SituMash
system. Teachers expressed that improving the SLT in such a u-learning environment is
interesting and can enrich the learning activities through their empirical observation. In
addition, the U-SLT assured teachers of using an effective teaching/learning paradigm.
They believe that the physical interaction and the real experience can deepen the
students’ understanding of knowledge cognitively.
Table 3. The Teachers’ perceived satisfaction of U-SLT
#
Question
SA(%)
A(%)
N(%)
D(%)
SD(%)
Mean
1
The user interface of the SituMash
system is clear and straightforward.
50.00
33.33
16.67
0.00
0.00
4.33
2
Are the learning services invoked
by the SituMash system in a U-SLT
conform to the original intention of
a traditional SLT?
33.33
50.00
16.67
0.00
0.00
4.17
3
Is the context representation
mechanism of the SituMash system
suitable to describe the learning
context in a SLT?
0.00
50.00
50.00
0.00
0.00
3.50
4
Is the situation reasoning
mechanism of the SituMash system
suitable to infer the students’
learning situation for a learning
objective?
33.33
50.00
16.67
0.00
0.00
4.17
5
Is the workflow planning
mechanism of the SituMash system
suitable to effectively plan the SLT-
based learning services?
50.00
50.00
0.00
0.00
0.00
4.50
6
Is the mashups optimization
mechanism of the SituMash system
suitable to effectively compose the
required learning services by the
lowest learning costs?
0.00
33.33
0.00
66.67
0.00
2.67
7
I encountered technical problems
while using the SituMash system.
33.33
50.00
16.67
0.00
0.00
4.17
Note. SA: strongly agree; A: agree; N: neutral; D: disagree; SD: strongly disagree.
4.3. Evaluation of the Learning Outcome
One of the important research questions in this study is that: Does the U-SLT improve the
learning outcomes? The authors designed this experimental research to demonstrate the
learning effectiveness of this study. In this experiment, 30 students were randomly
chosen from the high-level English communication junior course for the Experimental
Group, and 30 students for the Control Group. The Experimental Group proceeded with
the U-SLT-based learning and the Control Group proceeded with the SLT-based learning.
Before the experiment, a pre-test was conducted to compare the prerequisite knowledge
of the students in both groups; after the experiment activities, a post-test was performed
to evaluate their subsequent learning outcomes.

Knowledge Management & E-Learning: An International Journal, Vol.2, No.3. 325
Pre-test
The pre-test aimed to ensure that both groups of students had the equivalent
prerequisite knowledge required for taking the course. The examination questions of the
pre-test included 30 multiple-choice test items and ten open-ended test items. Table 4
presents the t-test results of the pre-test. Notably, the mean and standard deviation of the
pre-test was 82.4 and 5.87 for the Experimental Group, and 83.233 and 5.864 for the
Control Group. The p-value indicates that the two groups do not significantly differ at the
0.05 level. It is evident that the two groups of students had statistically equivalent
abilities in completing the programming course.
Table 4. Pre-test t-value and descriptive statistics for the two groups
N
Mean
S.D.
df
T
Experimental Group
30
82.400
5.870
58
-.550
Control Group
30
83.233
5.864
Post-test
The post-test was intended to compare the learning achievements of the two
groups of students after taking the programming course. Table 5 lists the t-test values for
the post-test results. Notably, the mean and standard deviation of the post-test were 92.5
and 5.283 for the Experimental Group, and 88.033 and 5.417 for the Control Group.
Following the t-test results it can be concluded that the Experimental Group achieved
significantly better performance than the Control Group after implementing the subject
approach (t = 3.233, p<.001), implying that the innovative approach is effective.
Table 5. Post-test t-value, effect size and descriptive statistics for the two classes.
N
Mean
S.D.
df
t
p
d
Experimental Group
30
92.500
5.283
58
3.233**
.002
.83
Control Group
30
88.033
5.417
**p<0.01
5. Conclusion and Further Work
Human-computer interaction is changing gradually. Students do not need to be restrained
by sitting in front of desktops and using the techniques of mobile computing and
ubiquitous computing. The interactions between humans and computers can be more
humanized because the computing units have fused into our physical environment. It
means that everyone can benefit from the convenience brought by computers anytime and
anywhere in the daily life. Based on the empirical study, the authors argue that a u-
learning system should be easy to use and intuitive to get information in order to support
learning and teaching in the platform, not just laborious in learning the platform itself.
Furthermore, it should be optimal in learning services provision with learning factors and
context that will get benefits from the perspective of cognitive learning theories.

326 Huang, A. F. M., Yang, S. J. H., & Hwang, G. J.
Situational language teaching is an effective instruction paradigm. However, in
the generation of u-learning, we need powerful and suitable techniques to achieve its
concepts and provide benefits to teachers and learners. Context constraints, such as
partner, event, time, location, and equipment, situate people in a special learning situation.
Being in different situations will confront different learning activities, and thereupon
students will need some hybrid application system to support them. The application of
situational computing is regarded as a situational mashups. With the contribution of this
study, the student's situation can be detected by situational mashups. Thereby, situational
mashups can accommodate to the students' activities in a ubiquitous environment, as well
as augment the capability of Web 2.0 for ubiquitous computing.
The authors integrated the related techniques to achieve the U-SLT paradigm and
promoted the concept of a ubiquitous learning school by finding that the colleagues or
participants around one student using the situational mashups will influence the learning
services configuration. The authors conjecture that the social networks of students will
influence their social-based learning activities, regardless of positively or negatively.
Therefore, more social network factors will be considered in situation reasoning.
Following the growth of mashups, more convenient composition mechanisms will appear
and more powerful platforms will be proposed. The usability and validity of the mashups
configuration will become an important issue. The authors will also devote to the
verification of mashups to make sure that the learners’ artifacts are safe and usable, and
extend the situational mashups to various pedagogies.
Acknowledgements
The authors would like to express their deepest thankfulness to Prof. Chin-Chung Tsai for
his valuable and constructive comments to the early version of this paper. This work is
supported by the National Science Council, Taiwan under grants NSC 98-2511-S-008-
006-MY3 and NSC 98-2511-S-008-007-MY3.
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