hi everyone my name is Ruijie Wang I'm glad to be here to present the tiis paper
titled modeling dyslexic students motivation for enhanced learning in e-learning systems as a brief
self-introduction I'm currently working as a post doc at Bournemouth university my phd project was
focused on motivation modeling and computation for people with dyslexia at De Montfort University i
have a background of both hci and psychology this work I'm presenting now is part of my phd project
this is the outline of my presentation today, first research background,
today e-learning systems offered us the opportunity to monitor users during their learning
process such as behavioral and mental states and learning progress in order to apply personalized
learning services and strategies to assist users learning. Highly motivated users are more likely
to respond more interactively with the system, stay longer in the system, and thus gain a high
level involvement and understanding of learning content in e-learning systems. Those with learning
difficulties such as dyslexia specifically are more likely to suffer from frustration
and lack of motivation due to the difficulties they experience frequently in learning process,
this is why we should incorporate motivational factors into user modelling
and personalisation in e-learning systems for such students with learning difficulties.
As a first step we want to investigate what are the key motivational factors
that affect dyslexic students' continued intention of using e-learning systems,
and how to quantitatively model motivation with these factors.
This is aim of the present paper, after this we can then explore the computation method
to assess users motivational states that are dynamically changing during the learning process
in order to apply personalised strategies to e-learning systems.
Methodology, the approach is to initially construct the model by integrating the most
relevant factors from existing theories with modifications to adapt to our research context,
following that an empirical study was conducted with dyslexic students to identify the relevant
factors in real world scenario and refine the initial model accordingly, this process is mainly
knowledge driven and qualitative. Afterwards we constructed a multi-item questionnaire
according to themes resulting from the qualitative modeling stage and applied structural
equation modeling to quantitatively test the effect of the motivational factors and determine
the interrelationships quantitatively in the model. Qualitative motivation modeling,
we conducted an empirical study by remotely observing a sample of 11 students with dyslexia
to use a prototype of mobile learning system called mYoutime, followed by individual interviews
the study intended to collect first-hand data from dyslexic students about all possible
motivations and barriers to use the software, the software provided it provided a concrete
real world context for this study, each participant did four learning tasks with quizzes
in the software, each is less than 15 minutes with learning content made of text audio and video.
In the meantime we also compared two features of the software that may be important to dyslexic
learners some users were allowed to have the learning content read aloud by clicking on a
button on the on the learning interface, but the others could not do that, in the
quiz users received feedback in the software showing whether an answer is correct or not, in
interviews we showed participants an alternative version of feedback with prototypes with levels
and badges showing their task performance as shown in this picture, in the interview
they were asked open-ended questions about their learning experiences to elicit their motivational
factors and barriers to use the software and continue the learning process in the software.
Using paper coding and semantic approach profiles are generated for each participant
containing their reasons for intention to use the software and their demographic information,
user motivations revealed for each participant were then related to corresponding motivational
factors retrieved from the literature, if no match was found in the prevailing literature,
a new theme would be proposed; secondly, the interviews transcripts were coded and analysed
using qualitative data analysis software called NVivo, to improve the reliability of the analysis
and enhance the objectivity of the findings, based on motivation themes revealed in the
qualitative study and based on existing theories and research in literature,
these factors and relationships between the factors were proposed and incorporated to
refine the initial motivation model. Next step is a quantitative motivation marketing stage,
we developed a series of statements on multi-item questionnaire according to the themes identified
from the qualitative modelling stage to examine the motivational factors and quantitatively
examine their relationships, participants were asked to assess the statements on five-point
Likert-type questionnaire ranging from strongly agreed to strongly disagree,
the questionnaire was reviewed by experts and pre-tested by dyslexia students and refined
accordingly before distribution. Data gathered were analysed using structural equation modeling
there are two typical approaches to SEM namely covariance based or variance-based SEM,
also named namely cb-sem or pls-sem, we firstly use the cb-sem but we found our
data does not fulfill normality assumption also our sample was relatively small 88 respondents,
so we also use the pls-sem to consolidate the results by examining if there's a huge
difference between the two approaches, the objectives of the two approaches
are exactly the same, first, to ensure the reliability and validity of the measurement model,
second, to examine the statistical significance of the paths in the structural model, so measurement
model refers to how measured variables come together to represent constructs, structural model
involves how constructs are related to each other. Exploratory factor analysis and confirmatory
factor analysis were also performed to test the validity of the measurement model. Results,
this is a motivation model resulting from cb-sem showing how different factors work together to
influence continued intention to use an e-learning system, all paths between factors were significant
showing past coefficient which is the standardized beta, and this is a motivation model resulting
from pls-sem, all parts are significant as well, we can see it's very similar to the last one,
so comparing the two result models and combining with existing theories from literature we grouped
these factors into extrinsic motivational factors such as perceived usefulness and ease
of use of the e-learning system, and intrinsic motivational factors such as self-efficacy and
attitudes towards school, and mediators such as learning and reading experience, consistently we
found that extrinsic and intrinsic motivational factors work together on continued use intention,
and mediators mediates the effects of the two group of factors. Among them visual attractiveness
and reading experience have a direct and strong effect on continued use intention.
From our findings there are some implications for design of e-learning system to address dyslexic
users' needs to improve their motivation to engage with e-learning system, in other words
we should apply corresponding strategies to e-learning systems to address all the factors in
our motivation model more references can be found in these papers the first one is the TiiS paper
I'm presenting today, the second paper shows how i developed a classification model for motivation
assessment using eye tracking data, the third one describes a software prototype that can provide
personalised feedback based on one's motivational states assessed by eye tracking data in real time
thank you very much