The objective of this study was to investigate if an e-learning environment may use measurements of the user's current motivation to adapt the level of task difficulty for more effective learning. In the reported study, motivation-based adaptation was applied randomly to collect a wide range of data for different adaptations in a variety of motivational states. This data was then utilised to extract rules for an adequate motivation-based adaptation to maximise expected learning success. A learning classifier system was used for the data analysis, generating rules for suitable and unsuitable adaptations based on current user motivation data. We extracted a set of twelve rules which suggest particular adaptation strategies based on real-world data. These rules could generally be embedded into existing psychological theories, namely the Zone of Proximal Development and the Yerkes-Dodson Law. In future research, we intend to evaluate these rules on further studies and develop concrete sets of adaptation strategies based on user motivation measurements. E-learning is a large part of the current educational process. A growing number of educational institutions utilise e-learning, such as learning games suitable to teach a particular (small) topic, online language courses, and even entire university programs using virtual classrooms. Some of these e-learning environments, free and commercial, are used in real instructional settings. Self-study language programs are a well-known example. However, most of these instructional systems still employ a 'one-size-fits-all' philosophy, where every learner, irrespective of their individual abilities and needs, is presented with the same content in the same way (e.g., Rey, 2009) . Although these learning environments may help students to learn, adapting e-learning environments to the individual needs of each student may help learners reach their learning goal even more quickly and effectively (e.g., Chen, Lee & Chen, 2005; Shute & Towle, 2003) . These adaptive e-learning environments have been researched for quite a while. According to Mödritscher, Garcia-Barrios and Gütl (2004) , the macro-adaptive approach, which allows the adaptation of some main components, can be traced back to the 1970s. Current developments in the field of computer science make it possible to 1120 Australasian Journal of Educational Technology, 2012, 28(7) build and thoroughly test ever more complex learning environments, which may take even more adaptation criteria into account. One of these criteria is the learner's current motivation. Motivation is an internal condition that activates, energises, and directs behaviour. It has been shown to correlate with learning performance in several studies. Schiefele, Krapp and Winteler (1992) concluded from a meta-analysis that measured interest can predict school achievement. Using a program that teaches tenth-grade students genetics, Song and Keller (2001) showed that a motivationally adaptive strategy promised a significantly higher performance and efficiency, compared to a motivationally minimised or saturated strategy. Keeley, Zayac and Correia (2008) conducted a study that showed a statistically significant, nonlinear relationship between anxiety and test performance of students enrolled in an introductory statistics course. We use a questionnaire to assess motivation in our study (cf. Rey & Buchwald, 2011), and a learning classifier system (LCS) to evaluate the influence of motivation on learning in the scope of a simple e-learning environment. LCSs are adaptive systems (Holland, 1975) which can learn general rules from exemplary input, using reinforcement learning (Sutton & Barto, 1998) and genetic algorithms (Goldberg, 1989) . Given enough input values and an extractable underlying data structure, LCSs are able to learn rules that are expected to maximise predicted payoff (for more information on LCSs, see e.g. Butz, 2006) . Moreover, the extracted rules can be used as a basis for developing fundamental motivation-based adaptation strategies in e-learning systems. The results of this work may help us understand the influence of a learner's current motivation on their learning success. Consequently, better e-learning systems may be built, which take these insights into account to better adapt to a user's motivation, and thus to help them learn more quickly and efficiently. We first discuss different aspects of motivation, motivational design and motivationbased adaptation. Next, we consider different theories relevant for learning and motivation, and introduce the conducted study in detail and present the generated results. We then introduce the LCS that was used to extract adaptation rules from the available data and look at the extracted rules in more detail, embedding them into well-known theories like the Zone of Proximal Development (ZPD) and the Yerkes-Dodson Law. Current limitations and future directions conclude this work. Motivation-based adaptation (cf. Keller, 2006) , as understood in this paper, aims to improve learning success by adapting the learning environment or learning tasks based on the learner's current motivation. It does not aim primarily to increase the learner's motivation, although it may try to influence the learner's motivation if motivation in turn promises to influence learning success. In short, motivational design aims to influence motivation, while motivation-based adaptation aims to utilise knowledge on motivation to influence learning success. Another important aspect of the integration of motivational factors into adaptive elearning is motivation detection, which concerns itself with the assessment of Another dynamic approach is the utilisation of standard reactions such as a request for help, or the act of giving up. Paralinguistic cues, such as facial expressions, body language, or intonation, can also indicate a learner's motivation and used for adaption (Sarrafzadeh, Alexander, Dadgostar, Fan & Bigdeli 2008) . However, the assessment of these cues might be laborious and may be considered intrusive by the learner. Two well-known theories which predict the influence of learner's expertise and leaner's arousal on learning success are the ZPD (Vygotski, 1963) and the YerkesDodson Law (Broadhurst, 1957; Yerkes & Dodson, 1908) . These theories might be adaptable to the influence of motivation on learning success. The original concept of the ZPD predicts that the highest learning success can be expected if expertise and difficulty are on a similar level (cf. Figure 1 ). If difficulty is too high or too low for the learner's current expertise, learning success will decrease. The theory of flow (Csikszentmihalyi, 1990) assumes that high difficulty for low expertise will lead to anxiety, while low difficulty for high expertise will lead to boredom. The assumptions from the concept of the ZPD and the theory of flow are integrated in Figure 1 . The figure includes anxiety, boredom and ZPD as a function of task difficulty (y-axis) and learner's expertise (x-axis). (Csikszentmihalyi, 1990; Vygotski, 1963) . The Yerkes-Dodson Law assumes an interrelation between arousal, task difficulty and performance. The law postulates that a certain amount of arousal is necessary to 1122 Australasian Journal of Educational Technology, 2012 activate learning. If arousal is low, performance will also tend to be low. The optimal arousal, i.e. the level of arousal that will lead to maximal performance, is on an intermediate level across the possible range. Higher arousal will lead to a decrease of performance again, so that the correlation between arousal and performance forms an inverted U-shape. For easy tasks, higher activating arousal is expected to result in higher performance compared to difficult tasks, where the optimum is reached for a medium level of arousal. Assuming that arousal and motivation overlap (remember that motivation is defined as internal condition that activates, energises, and directs behaviour), the Yerkes-Dodson Law can be applied to research considering the influence of motivation on learning success. Previous research suggests that motivation plays an important role in learning (Keeley et al. 2008; Song & Keller, 2001 ) and e-learning (e.g., Kim & Frick, 2011) . The objective of our study was to find plausible strategies for the adaptation of task difficulty, given learners' current motivations. We aimed to find the adaptation strategies for an adaptive e-learning environment which promise the best expected learning success. In total, 37 participants took part in the study, which took place in August 2010. Of the participants, 38% (14) were male and 62% (23) female. Ages ranged from 16 to 48 years, with an average age of 26.7 years (SD = 5.8 years). Twenty-one of the participants were students, four were pupils, three were working on their doctorates, two were currently unemployed, one was in early retirement, and six were working in various areas of expertise. Participants each received 6 Euros for their study participation. ELMA (E-Learning with Motivational Adaptation) is a micro-adaptive e-learning program, which has been developed for this study. It presents a fixed number of tasks and measures the learner's motivational state several times during learning. A simple adaptation, which reacts to the learner's current performance, tries to keep the task difficulty on a level that is consistent with the learner's expertise to avoid the negative impact of the expertise reversal effect (Kalyuga, Ayres, Chandler & Sweller, 2003; Kalyuga, 2007) . For the present study, a second adaptation was done randomly after each assessment of the learner's motivational state in order to collect a broad sample of data. This data was then used to extract rules for an adequate motivation-based adaptation. ELMA worked with a very simple interface, which consisted mainly of black text on white background (cf. Figure 3) . Some important information was highlighted using larger or bold letters. An animated progress bar indicated the time remaining for the learner to solve a task. This progress bar also used colors (green and red) to signal whether time was running short. Endler, Rey and Butz The flow of the program can be seen in Figure 2 . In the following sections, the various parts of the program are presented in more detail. We used self-assessed motivation via the "Questionnaire on Current Motivation" (QCM) which was introduced and evaluated by Rheinberg, Vollmeyer and Burns (2001) in our study. Satisfactory reliabilities for the QCM have been demonstrated for both the German and American versions with six samples (N = 944). Furthermore, several studies have supported the questionnaire's validity by showing that initial motivational factors were related to subsequent learning behaviours and outcomes (Rheinberg et al., 2001 ). The questionnaire consisted of 18 questions and four subdimensions (anxiety, probability of success, interest, and challenge), all containing 7-point Likert scales. In the questionnaire, the learner was asked to report their current motivation based on the previous block of tasks. The complete questionnaire assessed the learner's motivation at the beginning and at the end of the program, but was not considered for the motivation-based adaptation. For the second complete questionnaire the statements were altered slightly, to make them suitable for a retrospective view on the tasks. A reduced version of the motivational questionnaire was presented several times during the program to make sure that the program always captured the learner's current motivation. This reduced questionnaire consisted of four statements, covering each of the motivational factors. We chose the statements that had proven to give the best indication for each factor in the study conducted by Rheinberg et al. (2001) . In a principal component analysis, each of these statements showed a correlation of at least 0.75 with the respective motivational factor (.85 for anxiety, -.78 for probability of success, .79 for challenge, and .75 for interest). These statements were "Es ist mir etwas peinlich hier zu versagen." ("I'm a little embarrassed to fail here.", .85) for anxiety; "Wahrscheinlich werde ich die Aufgabe nicht schaffen." ("I probably won't be able to 1124 Australasian Journal of Educational Technology, 2012 solve this task.", -.78) for probability of success; "Ich bin sehr gespannt darauf, wie gut ich hier abschneiden werde." ("I'm very curious as to how well I will perform here.", .79) for challenge; and "Eine solche Aufgabe würde ich auch in meiner Freizeit bearbeiten." ("I would work on a task like this in my free time.", .75) for interest. The reduced motivational questionnaire followed after an interim test and was succeeded by a learning phase. The next interim test evaluated the quality of the chosen adaptation, using the interim test before the questionnaire to exclude the learner's prior knowledge., i.e. the learning success was defined by the percentage of correct answers in the current test compared to the percentage of correct answers in the test before the last learning phase. The questionnaire was presented immediately before the corresponding learning phase to measure the student's current motivation without any delay caused by instructions or tasks that may influence the learner's motivational state. Figure 3: The screenshot shows a task (type 0 task, i.e. the sequence of numbers task) of ELMA, which was presented to the participants Every task consisted of a presentation, five response options, and feedback, which explained the correct answer. When presented with a task, the presentation and the five response options were shown to the learner. The learner then had to choose the right answer out of these five options. Accordingly, there was a 20% chance that a learner would choose the right answer if they chose one randomly. No aids such as a Endler, Rey and Butz calculator or pen and paper were allowed when solving the tasks. A time limit for each task was set to 60 seconds. A green progress bar at the top of the program window reminded the learner of how much time they had left to solve the task. The progress bar turned red when time ran low, i.e., when less than 25% of the time was left. Once the learner chose an answer and clicked the continue button, or time ran out, the learner received feedback for this specific task. This feedback consisted of whether the task had been solved correctly by the learner, the correct answer, and an explanation of the correct answer. The program presented two types of tasks in six levels of difficulty. The types of tasks, and the tasks themselves, were taken from the Kognitiver Fähigkeitstest (cognitive capability test, KFT, Heller & Perleth, 2000). The test was originally constructed as a cognitive capability test for secondary school students, but, as the average percentage of correctly solved tasks in our study confirms, their demand on mathematical abilities proved challenging for adults as well. We used the tasks Q2, called type 0 here, and Q3, which we call type 1. In the study, the types of task were not adapted, but simply alternated to give the learner some variety and avoid boredom. In the type 0 task, also known as the sequence of numbers task, the learner was presented with numbers, and occasionally letters. The sequence was determined by a certain rule. The learner had to detect the (most obvious) rule and choose the next number of the sequence from the presented answers. The type 2 task, also known as the equation task, presented the learner with several numbers and mathematical operators. They had to combine these numbers and operators to form an equation, the result of which was one of the presented answers. All the numbers and operators presented had to be part of the equation. Operators ranged from addition, subtraction, multiplication, and division to bracket terms, square roots, and the power of two in the higher levels of difficulty. The pretest consisted of 12 tasks, one of every type and level of difficulty. It was used to assess the participants prior knowledge. The tasks were presented starting from type 0 and difficulty 0 with alternating type and increasing difficulty to type 1 and difficulty 5. The presented tasks in the pretest were identical for every learner. Each of the three interim tests consisted of six tasks, one of each of the levels of difficulty 1, 3, and 5 of each type. As every interim test was followed by a motivational questionnaire, and we wanted to influence self-assessment of motivation as little as possible through preceding task difficulty, we finished every interim test with a medium level of difficulty, i.e. difficulty 3. Therefore, the sequence of tasks was difficulty 5, difficulty 1, and difficulty 3 with alternating types. As in the pretest, the presented tasks in each interim test were identical for every learner. Different interim tests, however, presented different tasks. The first learning phase started with a randomly chosen task of type 0 and difficulty 0. Using the Simple Adaptation, as explained below, this enabled the learner to quickly reach an adequate basic level of difficulty, which served as a basis for the MotivationBased Adaptation. The following tasks were randomly chosen from the pool of learning tasks with the required type and difficulty. Every other learner was presented with a different sequence of tasks in their learning phases. The first task of a new 1126 Australasian Journal of Educational Technology, 2012, 28(7) learning phase was adapted with respect to the last task of the last learning phase. Every task was presented no more than once, unless the pool of tasks of a specific type and level of difficulty held no more unused tasks. In this case, an already presented task was reused. Tasks from the test phases were not presented in the learning phase. As stated above, the types of task were not adapted, but were instead alternated, to give the learner some variety. For adaptation, we concentrated on the level of difficulty. We limited the choice to three actions we considered appropriate for a learning program: increase the level of difficulty by one, decrease the level of difficulty by one, or leave the level of difficulty unchanged. We assumed that an increase or decrease of difficulty by more than one would lead to a change being too large for adaptation. The Simple Adaptation (SA) was applied within a learning phase. It reacted to the correctness of the learner's answers and adapted the level of difficulty if the last two or more tasks were of the same level of difficulty. The adaptation was applied as follows: If the learner solved the last two tasks correctly, we assumed that they mastered the current difficulty, and the level was increased. If the learner could not solve at least one of the last two tasks correctly, we assumed that the current difficulty exceeded the learner's abilities, and the level was decreased. If the learner solved one of the tasks correctly, no change was applied to the level of difficulty. This adaptation follows the ZPD concept in an attempt to avoid the negative impact of the expertise reversal effect. This means that we tried to adapt the level of difficulty to match the user's current expertise. The focus of this study was on Motivation-Based Adaptation (MBA). This adaptation of task difficulty was applied at the beginning of each learning phase. The objective of our work was to find plausible rules for MBA. These adaptation rules should be based on the motivation reported in the questionnaire given before the respective learning phase, and should maximise learning success. Learning success was calculated from the interim test following a learning phase. Prior knowledge was excluded, using the interim test preceding the learning phase. More precisely, we defined learning success as the difference in ratios of correct answers in the two interim tests. Consequently, learning success could range from -1 to 1. In our study MBA was applied randomly. That is the level of difficulty was randomly increased, decreased, or not changed according to the motivational states. This provided us with a broad sample of motivational states and adaptations from which we could extract those rules that promised the best learning success. Notice that a proposed increase (or decrease) in difficulty might not be possible because the highest (or lowest) level had already been reached. In this case, the e-learning program left the level of difficulty unchanged. Information about a learner's performance during the program was stored in a file. This information included the learner's answers to the motivational questionnaires and their results for every task presented. The stored results of a presented task consisted of the task's type and level of difficulty, the correctness of the learner's answer and the time the learner needed to solve the task. Of the stored information, the four motivational factors and the MBA of each learning phase were independent variables. Endler, Rey and Butz The dependent variable was the resulting learning success, which was computed from the test phases before and after the corresponding learning phase. The study took place on three sequential days with two to four groups per day and one to six participants per group. Six PCs running Windows XP were used to run the elearning program ELMA, which was implemented using Java, and to collect the data. Participants could work through the program in their own pace, apart from the 60-second time limit to solve a single task. The participants were also asked to fill in an additional questionnaire, which collected demographic data and comments. This data was tied to the participants' learning data but otherwise was anonymous. When the participants started the program, they were presented first with several text pages that gave an introduction to the program and the presented tasks. They were then given the pretest and the motivational questionnaire, which measured the learner's prior knowledge and current motivation, respectively. The first learning phase started with 10 tasks which were adapted according to the SA scheme, followed by the first interim test. Two learning phases formed the key part of the program. Each consisted of the shortened motivational questionnaire to assess the learner's current motivation, the learning phase of 10 tasks, and the interim test to measure learning success. At the end of the program, the complete motivational questionnaire was presented once more to gather more information about the learner's motivational state. The results were saved, and the learners were thanked for their participation on a last text page. All but one participant stayed within two times the standard deviation of the mean value for the average time needed to solve a single task and the percentage of correctly solved tasks. One participant took a shorter average time to solve a single task while showing a lower percentage of correct answers. This correlation can be expected and may stem from low motivation. Therefore, we did not consider these values outliers and kept them in the data set. Furthermore, no participant was able to solve more than 82% of the tasks. Similarly, no participant gave less than 31% correct answers, with the probability of solving a task correctly by randomly choosing an answer being 20%. This suggests that the overall difficulty of the tasks was adequate for the learners and floor and ceiling effects were likely to be negligible. Learning success ranged from -0.5 (in one case) to +0.5 (in three cases), with the theoretically possible learning success ranging from -1 to 1. In other words, the learning success showed a high degree of variability for different adaptations for different learners in different situations. This suggests that finding an appropriate adaptation for a specific learner in a specific situation may indeed have a large influence on the learning success. The average learning success was 0.0045 in the first learning phase and 0.144 in the second phase. This means that on average, learners only showed a very small improvement, with roughly 0.85 more correct answers in the test after the second learning phase compared to the test before the second learning phase. In the first learning phase, average learning success was even smaller.
Endler, A., et Al. investigated the possibilities of adapting motivation states into e-learning platforms and identified the existing difficulties that result from the lack of cogntive states' adapative features in e-learning platforms REF .
Towards motivation-based adaptation of difficulty in e-learning programs
Australasian Journal of Educational Technology
Computer Science