Use of Intelligent Tutor in Post Secondary Mathematics Education in the United Arab Emirates.

The purpose of this paper is to determine potential identifiers of students' academic success in foundation mathematics course from the data logs of the intelligent tutor Assessment for Learning using Knowledge Spaces (ALEKS) A cross sectional study design was used. A sample of 152 records, which accounts to approximately 60% of the population, was extracted from the data logs of the intelligent tutor, ALEKS. Two step clustering, correlation and regression analysis, Chi square analysis and ANOVA tests were applied to address the research questions. The data logs of ALEKS include information about number of topics practiced and number of topics mastered by each student. A derived attribute, which is the ratio of number of topics mastered to number of topics practiced is found to be a predictor of final marks in the foundation mathematics course. This variable is represented by the name mtop. Cluster classification based on this derived attribute resulted into three groups of students for which the mean values of the variable mtop are 0.80, 0.66 and 0.53 respectively. A moderately strong, positive and significant correlation was found between mtop and the final exam marks. Keywords cluster analysis, intelligent tutor, learning analytics, ALEKS INTRODUCTION It has been reported that many students spend a greater period of time in early years of the higher education especially in remedial or foundation programs; they spend more than expected without achieving the program requirements (Nasser, 2012) As Hansen et al (2006) explain, that secondary schools prepare students for university requirements but do less in preparing them to achieve at the level that universities require. Significantly, English language appears to be a difficult subject as most universities use that medium of instruction to teach science and non science subjects. Many students in the Gulf countries have not achieved a level of competency to enable them to successfully operate in English as a language of instruction and learning. Many programs, techniques, and methods are in place to support foundation year students in their learning in higher education among them is continuous assessments and review which are probably one of the key ingredients to improve student learning strategies to be used for enriching their learning experience as it has the cognitive as well as the motivational purpose (Ritter, Anderson,  2007) Timely feedback in assessments fills the gaps between the actual learning and expected learning outcomes (Chappuis, 2014) Formative assessment with feedback is particularly significant when students are uncertain about what is expected of them and when they need instructional guidance about how to move ahead (Nguyen, Hsieh Allen, 2006; Wood Wood, 1996) One claim is that the process by which students are monitored during instruction can help teachers provide timely feedback on students' actual learning. While formative assessments or continuous assessments followed by feedback can be used periodically to assess students' learning, it may not be feasible or practical to incorporate in large class sizes, (Chappuis, 2014) The first years in higher education, a great deal of stress is placed on the instructor to carry out continuous assessments, or formative assessments and provide timely feedback. More lately the use computer based assessment systems known as intelligent tutoring are widely used in secondary schools and higher education. These systems are web based and designed to run on multiple devices such as laptops, iPads and mobile gadgets. They can be used to conduct frequent formative assessments with appropriate and timely feedback to minimize the gap between actual learning and expected learning (Narciss Huth, 2004) It also engages students in authentic learning opportunities and can increase student participation and motivation in the learning process (Miller, 2009)  The Turkish Online Journal of Educational Technology October 2016, volume 15 issue 4 Copyright (c) The Turkish Online Journal of Educational Technology 153 A key feature of these software systems is their ability to record and store every learning activity occurring when a student interacts with the system. The data gathered for every user can be analyzed providing the "learning profiles" for each student or at the aggregate level. A learning profile is useful to understand students' study habits and their progress (Kotsiantis, Tselios, Filippidi Komis, 2013) Learning profiles can be detected by applying methods of Learning Analytics in which system generated large data logs are analyzed in order to understand students' learning activities (Siemens Long, 2011) The data generated support instructors to assess where the students are and where to go forward.  Computer based assessments In class, the instructor can engage students in student centered activities in the classroom through intelligent tutors. Students can access such systems online at any time or anywhere, for which they are expected to develop self regulatory approaches to succeed and use the technologies available to them in and outside the classroom (Aleven, Roll, McLaren, Koedinger, 2010; Nicol, 2006; Nguyen, Hsieh Allen, 2006) Web based intelligent tutoring systems allow students to practice, to have control over their learning and manage their time and interaction with peers and instructors (McArthur Stasz, 1990) Also the use of online computer based assessments, have several advantages over paper based assessments (Aleven, Roll, McLaren Koedinger, 2010; Balacheff Kaput, 1996; Hagerty Smith, 2005) they provide access to any number of students anytime and anywhere through any type of computer, such as a laptop, tablet or a smart phone. They provide a wider range of assessment techniques than the paper based assessments, for example, inclusion of graphics and multimedia. Students can provide their responses in various formats, such as drawing graphs on a digital screen, locating number positions on a number line by clicking on the webpage. The most significant aspect of computer based assessments is that individualized feedback is given instantly. More importantly, the software applications can generate questions randomly from a large bank of questions and different versions of assessments produced to tailor different levels of learning outcomes and practice questions required for mastering a topic (Shute Underwood, 2006) Moreover, such web based software can foster student centered learning by engaging students in meaningful learning activities and can increase students' engagement in learning (Chen et al, 2008; Chen, Yunus, Ali Bakar, 2008; Nguyen, Hsieh Allen, 2006; Schneider,  2013) Intelligent tutors Generally, a computer tutor or intelligent tutor establishes task related goals and guides the learner toward the goal. An expert tutor is capable of designing learning tasks to ensure that the student persists on the task and gains some new knowledge, the student when interacting with the computer tutor may heavily rely on the system to work out a problem as an act of educational transference (Thelwall, 2000) While early computer based learning was based on behaviorist learning theories that each application is taught as a separate learning objectives, into modules with separate objectives that are linked in such a way that the outcomes in one module can be used as an input into another. However more recent advances in cognitive sciences recommend adapting the constructivist learning theory and emphasize that "true" understanding as connected and generalizable knowledge wholes (Ritter, Anderson,  2007) According to constructivist learning paradigm, a student could cultivate independent and self directed learning and higher ordered thinking. Researchers as Chen, Yunus, Ali Bakar (2008) and McArthur Stasz (1990) have shown that computerbased or web based assessments had positive effects on students' mathematical learning processes especially where problems required analytical and critical approaches for solving them. In mathematics immediate correction and feedback can generally have substantial real time benefits to students as it gives them an opportunity to analyze the problem and readjust, reorganize, restate, and recalculate the problem work and move to higher levels of the taxonomy of educational objectives. The current and emerging technologies, such as intelligent tutors, which are supported by artificial intelligence techniques have an advantage compared to other information technologies (Chen, Yunus, Ali Bakar 2008; Chen et al, 2008; McArthur Stasz, 1990; McGatha, Bush, 2013) The intelligent tutors have the ability to integrate more than one medium, provide authentic and concurrent learning activities and provide academiccontent based support to a large student body. As reported in (Stiggins, 2001; VanLehn, 2011) human tutoring has an effect size of d 2.0 relative to classroom teaching without tutoring. This effect is known as the 'two sigma gain' Developers of intelligent tutors work towards achieving the same effect as human tutors by incorporating multidimensional tutoring with appropriate feedback and scaffolding techniques based on the knowledge of the subject and the knowledge of student's state of learning (Kao Lehman, 1997; Stiggins, 2001) Intelligent tutors' development is based on combining theories of cognitive science and techniques of artificial intelligence (Anderson, Boyle, Corbett Lewis, 1990; Ritter, Anderson, Koedinger Corbett, 2007;  The Turkish Online Journal of Educational Technology October 2016, volume 15 issue 4 Copyright (c) The Turkish Online Journal of Educational Technology 154 McGatha Bush, 2013; Miller, 2009) The intelligent tutors can provide interactive and personalized learning environment for students allow