April 2012 Volume 15 Number 2 - Educational Technology & Society

activities, and they documented the learning processes and important findings in the form of notes over a period of
eight weeks. The data was organized, and it classified the educational affordances relating to the proposed ubiquitous
learning environment and course. Finally, we obtained 36 open-ended questionnaires, 28 interview records, records
on 36 topics of discussion, 15 observation records, 6 team reports, and 36 yes/no questionnaires (response rate is
100%). Teachers and tutors helped students to understand the meaning of each question. The 36 response
questionnaires comprised 218 responses and were collected from the students.
Data analyses
The data were collected, organized, and reviewed by researchers who manually use the text mining technique
(Feldman & Hirsh, 1997) on features extraction and based on the following steps to obtain the educational
affordances:
Step1. Each student’s response was assigned a code; for example, r-q3-s7 indicates the response to question#3 by
student number 7.
Step2. Extract keywords about educational affordances manually from each student’s responses, such as the
keyword “data collection.”
Step3. Arrange the distinct keywords that were regarded as educational affordances, from all extracted keywords.
In order to obtain the perceived degree of each educational affordance, in quantitative data analyses, we designed the
yes/no questionnaire that asked students whether they used the tools/features/methods described in the questionnaire
to help in learning activities. The perceived degree of educational affordance is defined as the number of students
who reply “yes” divided by the number of students on an educational affordance.
Results and discussions
Analysis of data sources revealed eight actual educational affordances and five perceived educational affordances.
We employed a representative student’s response that was paraphrased to enhance readability as a sample for each
affordance, and the other students’ responses, interview records, and observation records were described in
discussion paragraphs.
Actual educational affordances
Unconstrained Knowledge Accession—knowledge construction is the ability to recall specific and isolatable
information from learned material (Bloom, 1956). M-Tools can enable students to retrieve learning materials from
the content server at any time and place, students through reading, comprehending, and memorizing those materials
to construct their knowledge. A student wrote the following in his survey response:
Student 6: I saw many crabs move sideways. I wanted to know whether all crabs move sideways. I
employed the browsing tool (m-Browser) to find the answer from MOBILE server, and I found that one of
the species, the soldier crab (Mictyris brevidactylus), is an exception. Using these ubiquitous learning
tools, I could easily acquire new knowledge.
In the researchers’ observation, the students were able to use the browser (m-Browser) and player (m-Player) to read
materials and play videos. These materials helped them further understand the features, living habits, and feeding
habits of animals.
Real-time Evaluation—evaluation is a treatment to assess the learning outcomes for achieving specific educational
purposes (Anderson & Krathwohl, 2001). The test tool (m-Test) could enable students to take real-time tests and to
improve their cognition. A student noted the following in his survey response:
Student 21: Answering questions immediately on the PDA is a new experience. On an electronic test tool
(m-Test), colorful graphics and sounds can be attached to each question. Compared with paper-and-pencil
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