April 2012 Volume 15 Number 2 - Educational Technology & Society

the words “pervasive” and “ubiquitous” as synonyms, but some papers show that there is a slight difference in two
learning environments, such as Lyytinen and Yoo (2002), Ogata and Yano (2004). According to Ogata and Yano
(2004), ubiquitous learning has integrated high mobility with pervasive learning environments. In this study, we
define U-learning as a learning paradigm which takes place in a ubiquitous computing environment that enables
anyone to learn at the right place at the right time, and it is adopted because math path activities need high mobility
of learning environment to situate students in authentic learning environments. In a U-learning environment, students
learn with a PDA, WebPad, Tablet PC or laptop, in indoor, outdoor, individual, and group situations. Mobile devices
and context-aware systems can sense the situation of learners, provide adaptive support to students, share and keep
the process of each student’s problem solving immediately. So, the above-mentioned restrictions of traditional paperand-pencil
based math path can be overcome by generating a U-learning environment.
Although U-learning seems to be able to improve the traditional math path, only a few studies have attempted to
apply this innovative approach to math paths. Most of the previous U-learning studies have been conducted on
natural science courses (Chiou et al., 2010; Chu, Hwang, Huang, & Wu, 2008; Chu et al., 2010; El-Bishouty, Ogata,
& Yano, 2007; Hwang et al., 2010; Peng, Chuang, Hwang, Chu, Wu, & Huang, 2009; Rogers, Price, Randell, Fraser,
Weal, & Fitzpatrick, 2005), language training courses (Joiner, Nethercott, Hull, & Reid, 2006; Liu & Chu, 2010;
Ogata & Yano, 2003, 2004; Zurita & Nussbaum, 2004), complex science experiments (Hwang, Yang, Tsai, & Yang,
2009), or museum learning (Hall & Bannon, 2006; Laine et al., 2009). And, mobile devices applied in these studies
are almost PDAs (Chen et al., 2008; Chiou et al., 2010; Chu et al., 2008; Chu et al., 2010; El-Bishouty et al., 2007;
Huang, Huang, & Hsieh, 2008; Hwang, Chu, Shih, Huang, & Tsai, 2010; Hwang, Kuo, Yin, & Chuang, 2010;
Hwang et al., 2008; Hwang et al., 2009; Joiner et al., 2006; Liu & Chu, 2010; Liu et al., 2009; Ogata, Saito, Paredes,
San Martin, & Yano, 2008; Ogata & Yano, 2003, 2004; Peng et al., 2009; Rogers et al., 2005; Yang, 2006; Zurita &
Nussbaum, 2004) or mobile phones(Chen & Chao, 2008; Laine et al., 2009; Milrad & Spikol, 2007). Additionally, it
is evident that these ubiquitous learning studies have aimed to integrate real-life situations with digital learning
environment during the learning process, but lack the scheme for individual assessment and remedial instruction. In
instructional practice, it is also imperative to investigate how to administer adaptive tests in the U-learning
environment, as well as to provide follow-up guidance based on the individual testing reports.
Therefore, this study attempts to develop an adaptive U-learning math path system which combines U-learning,
campus math path and the previously developed Knowledge structure based adaptive testing system (KSAT). This
system can extend the application of U-learning to mathematics learning in elementary schools. KSAT is the
improvement in computerized adaptive testing system of Diagnosys (Appleby, Samuels, and Treasure-Jones, 1997).
It can provide adaptive testing functions, as well as adaptive remedial instructions to improve the shortcomings of
testing and remedial education in previous studies. After establishing the adaptive U-learning math path system, the
instructional effect of this system is evaluated through experiment research. With regard to the learning content of
experiment analysis, only the “looking for patterns” unit in a fifth-grade math curriculum is taken as an example in
this study.
To sum up, there are three major differences between this study and previous literatures. First, this study applies Ulearning
to a novel exercise and it hasn’t been explored in the literatures. Second, the mobile device adopted in this
study is Tablet PC that is seldom used in previous applications. The reason of applying Tablet PC is that the
solutions or problem solving process of math items are hard to write down by small mobile devices such as PDAs or
mobile phones. Third, the proposed adaptive U-learning system provides adaptive testing functions, as well as
adaptive remedial instructions to improve the shortcomings in previous studies.
The goals of this study are presented as follows:
1. To design an adaptive U-learning math path system.
2. To compare the influence of adaptive U-learning math path against traditional paper-and-pencil based math path
on mathematical learning scores in the “looking for patterns” unit.
3. To compare the influence of adaptive U-learning math path against traditional paper-and-pencil based math path
on remedial learning scores of mathematics in the “looking for patterns” unit.
4. To explore the effect of adaptive U-learning math path method on the learners’ mathematical connection
abilities after learning the “looking for patterns” unit.
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