Nehad T. Ramaha is currently pursuing Ph.D degree in E-learning in Center for Instructional Technology & Multimedia in University Sains Malaysia, E-mail: ntar11_tpm013@student.usm.my.The research paper published by IJSER journal is about Assessment of Learner’s Motivation In Web Based E-Learning 1
ISSN 2229-5518
Assessment of Learner’s Motivation In Web
Based E-Learning
Nehad T. Ramaha, Wan Mohd. Fauzy Wan Ismail
Abstract—Due to the rapid growth of the use of computers and Internet in education a large number of Web Based E -Learning (W BEL) systems have been developed and implemented. However, most these systems do not consider the learner’s motivation level during the learning process, while motivation is one of the most important factors for student achievement in learning. In this paper we argue that motivation assessment is very important for W BEL systems, therefore more research is needed in this area. A critical review of previous research on motivation assessment in e-Learning systems is also presented, and finally suggestions of how to assess the learner’s motivation during a learning activity in a W BEL system. Benefits from our approach would maintain a learner’s motivation during the learning process resulting in a higher performance level within the WBEL system.
Index Terms— E-Learning, asynchronous e-learning, synchronous e-learning, motivation theories, Web Based E-Learning, motivation assessment, ARCS model.
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HE role of technology in education has evolved and radi- cal changes have occurred since the emergence of the In- ternet. The Internet has provided various ways of learning delivery where the web can be used as a medium for educa- tion such as Web-Based E-Learning (WBEL), Adaptive Hypermedia, and Intelligent Tutoring Systems. Learning via the web may enable everyone to obtain all types of know-
ledge, at all levels, at any time and in any place.
E-Learning is an education paradigm that is based on the elec-
tronic delivery of learning materials via electronic media, in-
cluding the Internet, intranets, extranets, satellite broadcast,
audio/video tape, interactive TV, and CD-ROM [1]. In an e- learning system, information can be delivered in two different methods. The first is the asynchronous method where stu- dents can acquire knowledge at any time and in any place;
they can learn following their own pace. The most popular forms of asynchronous e-learning are instructional websites, email, and forum. The second is the synchronous method where the teacher and the student interact in real time, which include real-time web chats and video conferences [2].
Studies have illustrated that motivation is a key element of education and plays an important role in the success of the
learning process, since motivation is regarded as one of the important factors that drive student‘s performance [3-6]. Ac- cording to Hrastinski synchronous e-learning learners felt more motivated than asynchronous learners since synchron- ous communication closely resembles face-to-face communica- tion [2]. Although there are similarities with motivational problems in all e-learning settings, there are specific motiva- tional challenges within each major system. One important challenge in asynchronous communication is drop-out rates that tend to be higher than in face-to-face settings, since learn-
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Wan Mohd. Fauzy Wan Ismail is currently working as Associate Professor
in Center for Instructional Technology & Multimedia in University Sains
Malaysia, E-mail: fauzy@usm.my.
ers often feel isolated and levels of learning interactivity are often considered trivial [7].
For a long time, motivation has been seen in an e-Learning filed as a matter of design. In other words, proper instructional design and suitable learning activities would engage all learners [8]. Although designing a motivating e-learning environment is important, keeping students motivated for the entire learning period is one of the biggest challenges, not only in e-learning, but also in all forms of learning[9]. However, while in tradition- al face-to-face learning and synchronous e-learning, teachers have direct contact with the learner, thus being able to analyze the learner‘s whole behavior and thus can infer his/her motiva- tional state. But in the context of asynchronous e-learning (e.g. WBEL) motivation detection is a more challenging process [9]. Information about the motivational state of the learner would allow tailoring content and enhancing the motivation of the learner.
Due to the importance of motivation as one of the major fac- tors that contribute in the success of the learning process; there has been an increasing amount of research interest that tries to detect e-learning systems learners‘ motivation. There are two types of learner‘s motivation detection model in these re- searches; one of these types is applicable for research purpose only such as Derbali and colleagues researches [10, 11] where they try to use some sensors to assess learner‘s motivation, and
the other type may be applicable for the purpose of motivation detection in e-learning systems such as de Vicente [12] motiva- tion model, del Soldato [13] motivational tactics, and Qu and Johnson [14] motivational states modeling. Although there are numerous research in the field of e-learning which tries to detect learners‘ motivation in e-learning systems, there is few research that has been conducted to assess a learner‘s motiva- tion in web based e-learning. In this context, there is a need for having effective web based e-learning systems that can assess learner‘s motivation in real time. This paper addresses new challenges for web-based e-learning: how to assess learners‘ motivation during the learning activity.
This paper is organized as follows: Section two provides the related work. Section three suggests a solution for assessing learner‘s motivation in web based e-learning. Finally, section
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four presents the Summaries.
Motivation has always been one of the most important factors for student achievement in learning [3-6], as students with high motivation engage more in learning activities and are more likely to complete a course [15]. Experienced teachers usually infer student‘s motivation from observational cues such as posture, gesture, conversational cues etc. which are difficult to be processed by computer systems [11, 12, 16]. Therefore, most of the researches in the field of e-learning are directed towards finding a way to assess motivation from cues that can be easily processed by the e-learning systems (e.g. learner‘s actions, time spent on a task, his/her statements about his/her level of motivation).
There are plenty of literatures on motivation in which many definitions can be found. Eccles and Wigfield [17] see the study of motivation as the study of action. For example, a stu- dent who studies hard may be described as ‗highly moti- vated‘, while a student who is ‗lowly motivated‘ may not study hard. Thus it seems that motivation can have an influ- ence on student‘s behavior [18]. There are a large amount of motivational theories and models exists in the literature, as a result, there are several attempts that try to categorize these theories.
According to Weiner [19] there are two main types of motiva-
tional theories: mechanistic theories and theories based on a cognitive approach. Mechanistic theories are based on the idea that a human works as a machine where their motivation is based on needs, drives and instincts. On the other hand, in
cognitive theories human motivation is based on thoughts and beliefs, therefore, they have choices with regards to their ac- tions. However, as de Vicente [12] state this is not an absolute classification as some theories share ideas from both kinds of theories.
Eccles and Wigfield [17] classify motivational theories into four categories. The first category focuses on beliefs about competence and expectancy for success (self-efficacy theory and control theories). The second category focuses on the rea- sons why individuals engage in different activities; this cate- gory consists of the following constructs: intrinsic and extrin- sic motivation, achievement values, interests and goals (self- determination theory, flow theory, interest theories, and goal theories). The third category combines expectancy and value constructs (attribution theory, modern expectancy-value theo- ries, self-worth theory) while the fourth category describes links between motivational and cognitive process (social cog- nitive theories of self-regulation and motivation, motivation and cognition theories, theories of motivation with volition).
As we explained previously, we believe that the issue of as- sessing the learner's motivational state is crucial for develop- ing a successful e-learning environment, but to do this is not straightforward. Therefore, most of the researches in the field of e-learning try to assess motivation from cues that can be
easily processed by the e-learning systems.
Table 1 presents previous research work on the assessment
of learner's motivational state in e-learning environments and
motivational factors used to indicate the existence of motiva-
tion, the learning variables measured, the measurement type,
the assessment type, and the motivational theories(or model).
As stated previously there are a large number of motivation theories to be found in the literature, thus, as previously men- tioned there are several attempts to categorize these theories. Methaneethorn [18] states that these motivation theories focus on identifying factors that are likely to influence motivation, and different theories focus on different factors. However, sometimes these factors are intertwined in their nature.
Although there are many theories found in the literature, only a small number of them have been practically applied to research in the area of assessing learner‘s motivational state in e-learning environment. As shown in Table 1 the theory of motivation that has been widely accepted by researchers in the e-learning field is that of Keller [20]. Keller created a theory of motivation and also, built a model named as Keller‘s ARCS
[21] to design and incorporate motivation in instruction. The acronym ARCS is derived from four categories of motivational factors Attention, Relevance, Confidence, and Satisfaction. Keller used existing research on motivational psychology to aggregate motivational concepts and theories according to their shared and discriminative attributes to constitute this model [22].
The first category of the ARCS motivation model is Atten- tion (interest), which refers to gaining attention, building cu- riosity, and sustaining active engagement in the learning activ- ity. This category has three sub-level categories: perceptual arousal, inquiry arousal, and variability. The second category is Relevance, which includes concepts and strategies that es- tablish connections between the instructional environment and the learners‘ needs and wants. This category has three
sub-level categories: goal orientation, motive matching, and familiarity. The third category of the ARCS motivation model is confidence (expectancy), which incorporates variables re- lated to students‘ feelings of personal control and expectancy for success. This category has three sub-level categories: learn- ing requirements, success opportunities, and personal control. The fourth category of the ARCS motivation model is satisfac- tion, which comes from being able to see the impact of learn- ing. This category has three sub-level categories: natural con- sequences, positive consequences, and equity. [21, 23]
Malone & Lepper‘s Taxonomy of Intrinsic Motivation Model [24] is another widely used model for the assessment of learners‘ motivation which can be used as guidelines for the design of intrinsically motivating learning environments (e.g. WBEL) [18]. The taxonomy focuses on several factors that can intrinsically influence a learners‘ motivation. These factors are: challenge, sensory curiosity, cognitive curiosity, control and fantasy. Social Cognitive Learning Theory [25] is also another sound theoretical base for the assessment of motivation which has been widely studied in several research disciplines.
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Table1. Previous research on the assessment of learner's motivational state in e-learning environment (adapted from Ghergules- cu and Muntean [9])
This section presents how motivation can be assessed during the learning activity in web based e-Learning. Investigating various research approaches for assessing motivation in e- learning, the most important motivational factors as shown in table1 was: confidence, effort, and engagement. The majority of related researches have taken into consideration time va- riables such as: the time spend reading pages, the time spend on test/quizzes, the time spent on performing the task and the average time between sessions. These time variables have been used to measure most motivational factors. However, the
previous studies did not clearly discuss the range of times values. In an learning activity this time range may be identi
fied by the teacher (e.g. minimum and maximum time to per- form the task). In WBEL this time variables can be measured easily by tracking learner activity. In this way, the barriers imposed by the need of the range for time variables may dis- appear in the WBEL environment.
This paper proposes that motivation can be assessed in WBEL in real time by taking into consideration motivational factors such as: confidence, effort, and engagement. Next, a solution on how these factors can be assessed is presented.
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According to De Vicente [27] confidence refers to ―the stu- dent‘s belief in being able to perform the task at hand correct- ly‖. In e-learning, student confidence/competence was meas- ured using self reports questionnaire and dialog based interac- tion. An example of a dialog based interaction is presented in Figure1. Although this way may be very good to infer learn- er‘s confidence level, it‘s too boring to answer questionnaires frequently. Therefore we need to decrease the number of ques- tionnaires and dialog.
Figure1.Dialog used in assessing motivation in e-learning [12].
According to Del Soldato [13], less confident learners are likely to: 1) Avoid tasks perceived as difficult; or 2) Give up a task before attempting to perform it. The first point can be detected by interaction between the tutor and the student such as present the student with options of answers that explicitly mention the difficulty of the task (e.g. ―No, thanks, it is too difficult‖, ―I prefer an easier problem‖). Possibility of student giving up can be defined in terms of help requests and the number of steps in the problem solving process.
Zhang and colleagues [28] used time variables as indicators
for student confidence such as times to do the task. They state
that if a learner can start to do the task quickly, it means that
he/she has the assuredness to success, and he/she has high
confidence. Time is a good indicator of student confidence but
it‘s not always accurate, for example if learner takes less time
in performing task it‘s an indicator of high confidence but maybe the opposite is true; he/she rushing because the task is too difficult and he/she have low confidence. Therefore we need to ask the learner about his level of confidence.
Qu and Johnson [14] state that there are three major sources of information for a human tutor to infer learner‘s confidence:
1) the learner‘s hesitancy in performing actions after reading the tutorial; 2) the history of task performance (for example, how many tasks the learner has successfully completed.); and
3) the frequency of the learner‘s requests for help on certain
tasks. For example, if the learners perform the task after read-
ing the tutorial without much hesitancy, this implies that they
must have high confidence.
In this paper we propose to use the time variable as indica-
tors for student confidence combined with other indicators
such as help requests and the number of steps in the problem
solving process. Additionally dialog based interaction may be
used in more complicated situation.
According to De Vicente [27] effort refers to the amount of work that the student is doing in order to perform the learning activities. De Vicente used the level of student giving up and
student performance as indicators for student effort level.
Del Soldato [13] tries to classifying students‘ effort as a
function of their persistence to solve the problem and requests
for help to perform the task. he assumed that persistence to
solve the problem can be measured through the number of
attempts to get a solution, or steps performed, so that many
steps reflects a greater degree of effort from the student. In
addition to the number of steps performed, a student who
requests hints from the tutor or accepts help offered by the
tutor spends less effort than students who try to perform the
task on their own.
Qu and Johnson [14] state that a human tutor can infer the
learner‘s effort for a task by estimating how much time the
learner has already spent on this task. Therefore based on how the human tutor infers the learner‘s effort during in-person interactions, Qu and Johnson derive inference rules to detect the learner‘s effort.
In this paper we propose to use the time variable such as time spent on the task as indicators for student effort while other indicators such as the number of attempts to complete a task and requests for help to perform the task may also be combined as indicators.
Cocea [16] see engagement as an indicator of motivation, where the person is motivated to do the task he/she is en- gaged in, or the other words, if the person is disengaged, he/she may not be motivated to do the task. Cocea proposed to use behavioral cues detection as indicators for disengage- ment such as browsing fast rather than reading, skipping sec- tions, non-systematic progression, and answering questions quickly (in less time than the minimum required time for at least reading the questions).
Hershkovitz and Nachmias [32] used the time variables as
indicators for student engagement such as time on task per-
centage (Total time of active sessions divided by total time
frame documented.) and Average session duration as time
variables.
In this paper we propose to use cues as indicators for en-
gagement/disengagement to include browsing fast rather
than reading, skipping sections, and time to perform the task
or answering the questions.
Nowadays, the technological developments significantly im- prove the area of e-learning. In this paper various research approaches in assessing learner‘s motivation were presented, these approaches considered various motivational factors. Assessing motivation during the interaction with WBEL sys- tems is challenging because information about learner‘s ob- servational cues such as posture, gesture, and conversational cues are difficult to be collected. Therefore, the emphasis of this research is to assess motivation by analyzing other types of information such as times variables, learner‘s actions, and learner statements about his/her level of motivation. However future research may extend this model to become a general model with consideration to the variety of actions that could
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occur in WBEL environment. It‘s hoped that our proposed approach would be able to sustain a learner‘s motivation dur- ing the learning process resulting in a higher performance level within the WBEL system.
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