| Mobile Education - A Glance at the Future: 4 Data analysis | |
| |Contents
| Abstract
| Preface |
1 Introduction
| 2 Theoretical foundation for mobile education
| 3 Methodology and data collection
| 4 Data analysis
| 5 Conclusion
| References| Print out from the forum | |
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Data analysis "If I had thought about it, I wouldn't have
done the experiment. The literature was full of examples that said
you can't do this." This chapter looks at how data collected in each of the three phases
of our research was analyzed. 4.1 Open questions (phase 1) The margin of error resulting from this initial analysis is marginal since we feel we did not leave anything out, but included more than one answer in one alternative. Although we had come up with a final list of alternatives that would be sent back to respondents for phase two of the study, some of them were still somewhat similar but difficult to categorize as a single alternative. This was often since it gave different aspects of the same thing, such as the different perspectives of wireless data transfer. The respondents however may or may not feel the same way. This is something that is very clearly seen from the answers from question one, where the top four rated alternatives are about wireless data transfer and cost. 4.2 Top-10 lists and online asynchronous
forum (phase 2 & 3) In coming up with the top-10 list, we used a rating system that was governed by Miller’s information processing theory (Miller, 1956). The theory postulates that the human short-term memory can hold or effectively process five to nine chunks of data: Hence the notion of seven plus or minus two. A chunk is any meaningful unit. Thus, we awarded points to the top-10 lists in the following manner: • Number 1 in the list was worth 3 points. This emphasizes that
number one in any list is the most important item. The results of our rating are presented in section 3.5.2 or appendix F. A high rating indicates both coherency and acceptance to the placement of each of the listed items. The aggregate score in the column abbreviated “Tot.” determines where in the top-10 list an alternative will be placed. The highest aggregate score means that the alternative is rated first in the top-10 list. For instance, in questions three and four, “increased flexibility” has a first place in the top-10 list and scores 8 and 10 points more respectively to the number two in the listings (see section 3.5.2 or appendix F). The biggest source of error in this phase is typing errors. This results
form manipulating raw data received from each respondent, and reorganizing
them into one table that showed all individual results, and later to
summarize top-10 lists. 4.2.1 Analysis of research question
1.
The list for this question has very similar values within the list. This is due to the fact that respondents had 25 alternatives that were almost similar. As a result, it gave more leeway to the distribution of the way the alternatives were selected in creating a top-10 list. Some postings from the mLearning discussion forum that assert the positioning of the items in the top-10 list above follow: There is some activity on PDAs but little on mobile
telephones. In the future it may be possible to write materials in
XHTML and stream the content to either type of device, and this would
eventually include e-learning courses.
For many purposes the screen size is a problem...but
maybe we should look for other solutions than larger screens... I believe that voice synthesis could be a very good
solution for many purposes. I believe that I would appreciate to use
earphones listening to my PDA when it is reading an article for me
at the bus...I guess that this very soon will be available for the
PocketPC version of Microsoft Reader... Based on the top-10 list in section 3.5.2 or appendix F, it seems that the most important technology to impact mLearning is wireless data transfer. Communication costs were also sited as an important factor that would determine how fast mLearning would be adapted. 4.2.2 Analysis of research question
2 There is not much difference in aggregate points down the list, but the top 5 are definitely worth looking into for further research.
It is worth finding out what the user expects from a user-friendly device and the learning material presented. Quinn (2000) has described his ideal mobile device (see section 2.3) for mLearning. Not all the features he specifies are present on today's phones, but a lot of research is being done in this area by phone manufacturers. Nielsen (2000a) further reiterates the need for better usability by emphasizing the need to have better screens for viewing web content on mobile devices. Dr. Georg Ströhlein supports Nielsen's (ibid.) view with the following contribution: Both PDAs and smartphones available today do not
meet the minimum requirements of most people on learning media. The
level of complexity and interactivity of the material is far too low.
So the small activities aren't really surprising. We asked the question on what properties the respondents thought would impact the adaptation of mLearning. One of the respondents however did not think that the properties by themselves were adequate. The device could be pedagogically ideal for studying but if it is not available it will not be used. The adaptation momentum of mLearning depends on
how fast mobile devices and services in general spread to the public.
Mlearning in itself is not capable of reaching the necessary market
penetration of mobile devices. The penetration rate depends on the
availability of useful and inexpensive devices and services. #5 on the list is supported by Nielsen's (2000a) view on the ideal response times. Nielsen (ibid.) reccomends a maximum response time of ten seconds (see section 2.3). To support multimedia and streaming video, the student and tutor will require 3G networks at a reasonable cost. Content containing streaming video and are rich in multimeda effects help the student recall data or visualize the practicability of what he or she has read. 3G networks can thus be particularly useful in helping the student achieve level 1 goals in Bloom's taxonomy (see section 2.2.1 for an overview of Bloom's taxonomy). 4.2.3 Analysis of research question
3 We feel that the added flexibility is both good and bad. Good - in the sense that the students may study anytime and anywhere since they have the course with them at all times. The flexibility gives students a way to utilize the small breaks in the day for studying. On the other hand it is bad in the sense that the student may end up using much of his free time studying. This may result in social isolation, and stress. One can thus question whether this is a good way of studying. We believe that if the studying only takes during short breaks in the course of the day it may not be a very efficient way of studying. If this was an addition to (or took some time off) the study time the student intended to use later on that evening, it would be efficient and useful. The studying on the move alternative only made it to an 8th place on the top-10 list, which lead us to believe that respondents attached a broader meaning to this term. We also see learning will take place in other environments as a flexibility issue. This is the reason we believe the respondents rated “higher demand on the students and their motivation” as the third most important change for the students. We support our assumptions using quotes from the forum. Thus: The flexibility of mLearning will make it possible
to learn almost anywhere. The learner and the teacher may choose the
best place themselves. Some people need quite surroundings to be able
to study, other people can study anywhere. mLearning gives the individuals
the opportunity to choose when and where they want to learn. We are not certain that it is healthy to spend the few pockets of free time during a hectic day for studying. The short breaks can for some people be necessary to relax for a while and lay plans for the rest of the day. Using these pauses studying does on the other hand release time which can be spent with the family. Interestingly, it has been argued that so called
non-places (airport terminals, hotel rooms ...)are very suitable for
concentrated working: There is nothing other reasonable things to do,
so it is easy to concentrate to work with your device. In general,
people work mobile in places such as public transport, cafes, lecture
halls during lectures (I have empirical evidence of that) and home
(the most common place being a sofa while watching television) etc. It may be a paradox, but I believe m-learning is
most useful at home…simply because I spend much of the time available
for learning/teaching….at home. So, I would appreciate to be
a more mobile learner/teacher at home…reading articles in bed,
answering e-mails in the garden, checking contributions to the discussion
forum while I eat breakfast in the kitchen etc. I would prefer to do
this, using a tablet PC with wireless, high-speed access to my private
local area network. How are we going to fit our learning material to the new devices? This is one of the most often addressed questions in mLearning. Most research on mLearning revolves around what the small screen is suitable for but not when it would actually be used. Whether mLearning is going to be an alternative or an addition to other forms of learning is an important question to ask one self when developing course content. Some of the respondent addressed this issue to some degree in the forum. My main concern is that to many people are thinking
about how to port the same ol' stuff on new devices. What we need are
not to look to PDA and mobile phone providers for more powerful machines.
Educators and researchers must look into new pedagogical models to
understand learning in a mobile setting. New methods to support learning
activities. New understanding of the learning needs. New ways to understand
the outcome of the activities and so on. I think we should ask and try to answer an even
more basic question: under what circumstances will learners prefer
mobile learning to other forms of learning? 4.2.4 Analysis of research question
4 We believe that it will be helpful to implement applications to report and track the work the teacher does in each mLearning course he is tutoring. This may reduce the paperwork thus encouraging the teacher to report more often. Alternatively, task can be automated using software solutions developed for the mobile unit. Shotsberger and Vetter (2001) express similar views on teacher reporting. It may even lead to a change in the salary-system where the results and actual workload is measured and the salary is automatically calculated and reported to the teacher on a regular basis. Like the student, increased flexibility is the alternative respondents rated the highest when asked about how mLearning will change the teacher’s work situation (see section 3.5.2 or appendix F). The second choice is ten points lower - Classrooms can be extended to the real world. In third place - More rapid answers to the students’ questions will be demanded. However, the teacher may see the second place in the top-10 list in question three (learning will take place in other environments) as similar to the extension of the classroom to the real world. The ability to study in different environments must be of great value not only to the student who may see how it works in real life, but also to the teacher that now has live exemplifications of a problem. The negative change foreseen by the respondents that is of great importance is rated as high as number 4; no clear separation of work and value time. One can only find out how true rating number four is through personal experience. Responses on this issue included the following: From my viewpoint as a teacher I see most advantages.
As a tutor, I am terribly busy and often away and on travel. Being
able to work and communicate gives me much better opportunity to use
spare time. I can comment on assignments whether in the airport, on
the plane or at my summer house. I know what workload waits for me
and get no surprise when coming back. I do not experience that students
expect quicker feedback than in Internet based learning generally,
but of course they may become positively surprised with quicker feedback
also during vacation and holiday time. Some respondents predicted that teachers would be under pressure to support students promptly. This is because students are equipped with communication devices that allow them to be in touch with the teacher at all times. I have plenty experience as a mobile teacher (at
least if you include teaching via Laptop PC and mobile phone). For
at least 5 years I have communicated with my Internet students with
this kind of equipment. This means that the students were not necessarily
as mobile as their tutor. During the last years I have taught 3 different
courses via Compaq IPAQ and mobile phone (also mobile students). mLearning will probably give the teacher more work
as the students can study at any time, anywhere and the teacher therefore
need be accessible almost all the time. Rapid answers to the students’ questions
are important and therefore the teacher will have to be available also
outside the office hours. Paulsen (2001) offers another perspective on this issue. He advocates the development of better student support services that are designed to take on some of the teacher’s functions. These may include services such as discussion forums where students and teachers may discuss several aspects of a given issue. These answers will be available to all students and teachers. The mLearning technology will be a benefit for the
teachers who like to travel and spend more time away from home and
work. But it may as well terrorize teachers, since they never will
be able to escape their students.
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