Mobile Intelligent Tutoring Systems Education Essay

Published: Last Edited:

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Mobile intelligent tutoring systems have the potential to deliver low-cost, one-to-one assistance to learners outside of the traditional classroom and computer lab settings. The focus of this paper is to outline the use of Mobile Intelligent Tutoring Systems in supporting the Mathematics human tutors in secondary schools and the role that mobile devices can play in disseminating and supporting the knowledge gained by intelligent tutors. The paper reviews desktop Intelligent Tutoring Systems and how the same can be used in mobile devices. The final part of the paper examines the challenges faced in the development of Mobile Intelligent Tutoring Systems.

The general performance in mathematics among secondary school students in Kenya has not been impressive for many years (KNEC, 2000). Much has been done and said with aims of improving performance with little success including introduction of "Strengthening of Mathematics and Science in Secondary Education (SMASSE) Project", launched in Kenya in 1998 and funded by Japan government. It is aimed at the improvement of mathematics and science education through In-service Training (INSET) for teachers (Nancy, Alice, 2007). Poor performance is attributed to several factors among them attitude of students and teachers, lack of teaching facilities such as books and inadequate remedial or follow-up tutoring in most schools. Whereas there is need to help learners develop a deeper conceptual understanding through such techniques as tutoring when they are learning a new domain (Lane, 2006), that is wanting.

Mathematics is a difficult subject both to teach and learn. Mathematics is also a subject, which requires hard work, and lot of practice - the overriding aspects for learning mathematics. Learning mathematics comprises first receiving facts, principles, and then learning how to apply them (Garry, 1996). Teachers may want to spend more time with students but human resource is usually the main constraint due to high student-to-teacher ratio witnessed in schools.

The wide use of cell phones in society has led researchers to investigate methods to employ mobile devices in education (Castells, 1999). Currently, there are around 16 million cell phone subscribers in Kenya with the number projected to go up following licensing of fourth mobile operator - YU (Communication Commission of Kenya).

According to Brown (2003) and Kam etal (2009), the mobile device has been argued to be an appropriate tool for educational delivery in the developing countries. The argument behind this is that mobile device is low-power device that can be used in places without electricity. Although mobile device such as cell phone is largely purchased for voice communications - which users rely on for their social and economic needs - it is also able to run educational software that support visuals and voiceovers (as cited in Kumar, 2009). Most of all, the cell phone is the fastest growing technology platform in the developing countries. There are 2.2 billion mobile phones in developing regions like Africa and India, as compared to only 11 million desktops (CNN, 2009).

Teaching students on a one-to-one basis significantly influences the degree of knowledge and skill retained by the student. Bloom (1984) suggests that one-to-one tutoring is the most effective strategy known, generally yielding two standard deviations better performance than traditional instruction.

For the purpose of this paper, the term "mobile device" comprise of standard cell phones, smart phones (those utilizing an operating system providing voice services as well as additional data processing applications), and personal digital assistants (PDAs) - providing data processing without voice capabilities. Whereas laptop computers are portable, users interact with them in ways that are more similar to desktop computers than they do with smaller devices e.g. use of keyboard. Thus, it does not fall under 'mobile device' category.

Table 1.1 Comparisons of Desktop and Mobile Tutoring Systems



Mobile Device


Full size keyboard

Multi-window design

Small Keyboard

Single-window design


14+ inch display

2-5 inch display


Ethernet, Wi-Fi

Wi-Fi, Cellular



Client only


Classrooms and computer labs

Anywhere and anytime

2. Intelligent Tutoring System

Hafner (2000) defined Intelligent Tutoring System (ITS) as "educational software containing an artificial intelligence component. The software tracks student's work, tailoring feedback and hints along the way. By collecting information on a particular student's performance, the software can make inferences about strengths and weaknesses, and can suggest additional work." ITSs have been used to aid students with homework, test taking, and assessment (ISTE, 2007).

An ITS can be used to enable the students work independently, to improve their understanding of concepts within related domain, and to take progress of problem solving ability for each of them (Martin, 2001). On the other hand, an ITS can be able to assist not only the students but also the teachers in developing and managing courses (Shin, Norris and Soloway, 2006). According to Korhan (2006), " Intelligence involves mental capabilities such as the reasoning ability, planning, solving problems, thinking abstractly, comprehending ideas, and learning". Moreover, it is related to creativity and personality of the person according to psychology. Conversely, mathematics is as a nightmare for many students. This may lead to students doubting their creativity, talent, and motivation when studying mathematics. In this sense, the tutoring systems must have the capability of real teachers, and it must act like human tutor in a class. ITS can raise up the effectiveness of teaching mathematics in a class (Kinshuk, 2002) and therefore regarded as one of the subjects in Artificial Intelligence (AI)

3. Background of ITS

Computers have been used in education since 1960s (Martin, 2001). Intelligent Tutoring Systems are computer systems designed for support and improvement of learning and teaching process in the domain knowledge.

Even though Intelligent Tutoring Systems began with Computer-Aided Instruction (CAI), they differ from them in some ways. Firstly, the interfaces, in CAIs, are ever static for each student and the information presented to each student is exactly the same for all the time (as cited in Mitrovic et al., 2007)

According to Koedinger et al. (1995), ITSs use the knowledge for pedagogical process so that the system tries to determine what the student knows or does not know. Contrary to ITSs, CAIs have assumptions about what the student knows. Therefore, the same curriculum is presented to students in CAIs, even though the preceding knowledge is necessity for a student.

The other difference between them, according to Koedinger et al. (1995), is with the feedback system. Some CAIs have the capability of asking questions to students. However the feedback system of them is limited to indication of whether the student answer was correct or wrong, only. ITSs, on the other hand, try to determine the students' weaknesses on a topic using the domain and student model as shall be depicted in section 4 below.

Most Computer-Based Instructional (CBI) applications and systems, including ITS, still reside primarily on the desktop. According to Eamon (2004), ITS have been shown to be highly successful in improving student learning in the classroom. When ITS is integrated into school curriculum, students use the tutors during school hours in computer labs and classrooms.

The expansion of the desktop ITS to the mobile learning world of mobile will, undoubtedly, provide great benefit for students and teachers alike. A mobile intelligent tutor has the potential to deliver the significant advantages of intelligent tutoring systems to a wide audience of learners and expand tutor use to outside of computer labs and classrooms thus providing robust and flexible learning opportunities to students "anywhere" and "anytime" (Farooq etal, 2002). It will also be of help for students on the move such as nomads who may not get enough human tutoring in class besides enhancing student-centred learning.

4. How Intelligent Tutoring Systems Work

ITS for mathematical problems was planned and designed to facilitate students in learning and diagnose on student's errors and effectively generate explanations for those errors (Burns, Capps, 1988) and offer a student monitoring system that includes learning progress and relevant statistical data.

The goal of ITS is to provide the benefits of one-on one instruction automatically and cost effectively.

Like any other training simulations, ITS enables participants to practice their skills by carrying out tasks within highly interactive learning environments.

However, ITS goes beyond training simulations by answering user questions and providing individualized assistance. Unlike other computer-based training technologies, ITS systems gauge each learner's actions within these interactive environments and develop a model of their knowledge, skills, and expertise. Based on the learner model, ITSs tailor instructional strategies, in terms of both the content and style, and provide explanations, hints, examples, demonstrations, and practice problems as needed (James and Sowmya, 2007)

Intelligent Tutoring System

Student Model

Expert Model



Instruction Model

Figure 4.1 Intelligent Tutoring System Model

Intelligent tutoring systems have their foundation in the artificial intelligence, more specifically expert systems, and computer assisted instruction disciplines. Burns et al. (1988) describe the "intelligence" of this software as the collection of the five subsystems shown in Figure 4.1 above.

The first is an expert model representing the domain knowledge or subject matter expertise. This knowledge comprises the understanding of the subject matter that an expert has in the tutored area i.e. expert model simply represents the expert knowledge and the ability to solve problems within a domain.

The second model is the student's. This model represents the knowledge, skills, behaviour and other attributes of a student learning the domain. This model let the ITS know who it's teaching (James et al., 2007) and tries to determine student's mental states. This module generates the student model with all information about the individual learner. It provides the information such that what the student knows or does not know, any misconceptions, degree of forgetfulness, reasoning skills etc. (Korhan, 2006)

The third is the instruction model, which is responsible for recognizing student input and responding to student actions i.e. enables the ITS to know how to teach, by encoding instructional strategies used by the tutoring system. The instructor model selects the most appropriate instructional intervention based on the knowledge of a student's skills, strengths and weaknesses, participant expertise levels, and student learning styles. Additionally, the instructor model may also choose topics, simulations, and examples that address the student's competence gaps. It is also known as pedagogical or tutor module (Martin, 2001)

The fourth is the instructional environment or domain that provides support to the learner. It consists of the activity and tools, and to some extend the situation, provided by the system to facilitate learning.

The last component is the interface, an essential component that provides the means by which the user can communicate with the system. It is the integration of the models that separate ITS technology from other forms of computer-aided instruction (Heffernan, Koedinger and Aleven, 2003).

According to Trojahn et al. (2002), ITS have the instructive approach in which instruction is understood to be the transmission of knowledge requiring the teacher/instructor to monitor the student constantly, especially in the problem solving processes. It takes into account the capacity for learning and the knowledge of the student in that subject.

ITS's are adapted to each student by means of their diagnostic skills which examine the student's knowledge and the structuring and presentation of knowledge. They also make use of a variety of techniques to hold the user's attention (equated to human tutor motivation) and facilitate the transmission of the desired knowledge. Intelligent training systems also share this approach, although in these cases the processes are aimed more towards specific problem solving activities. The tutor guides the instruction process according to traditional practices (UPGRADE, 2002).

Knowledge is a key to intelligent behavior and, therefore, ITSs are said to be knowledge-based because they have: (i) domain knowledge, (ii) knowledge about teaching principles and about methods for applying those principles, and (iii) knowledge about methods and techniques for student modeling (S. Stankov et al., 2007)

It is important to note that ITS is an interdisciplinary field that investigates how to devise educational systems that provide instruction tailored to the needs of individual learners, as many good teachers do (Conati et al., 2002)

There are three types of knowledge that an intelligent tutor (human or artificial) needs to have to be able to aid student learning: (i) knowledge about the target instructional domain, (ii) knowledge about the student, and (iii) knowledge about the relevant pedagogical/instructional strategies.

5. Mobile Intelligent Tutoring System

According to Brown (2009), Mobile ITSs have not received extensive research. There has been little research aimed at identifying how to adapt the desktop tutors and which aspects of the tutor to change, as aspects of desktop tutors require modification for mobile device content delivery.

The delivery of ITSs on mobile devices in Kenya has the potential to provide the significant advantages of intelligent tutoring systems to a wider audience of learners thus helping in bridging the digital divide.

Some secondary schools provide Internet and computer access to students but a deeper assessment reveals that the presence of technology does not equate to effective use of the technology (Yong et al, 2006). Among the several factors hindering use is the student-to-computer ratio in schools. For those schools with computers, it is reported that no school has one computer for each student with the lowest computer-to-student ratio being approximately 3-to-1 (Christopher et al, 2007). On the other hand, nearly all students can access the mobile phones making it possible for schools to make use of handheld computing to coordinate technology use between home and school for the students. This trend is also pinpointing of the potential that mobile and handheld devices have to deliver a one-to-one computing solution to the education community (Quinn, 2000).

By using mobile devices, schools without the financial resources to invest in and maintain large computer labs can have the ability to provide learners with ITS technology. One remarkable merit is that students can easily carry the tutors between home and school besides sharing the mobile ITSs between students in the same school thus enabling 'everywhere' and 'anytime' learning (Facer, Faux, McFarlane, 2005). The portability of mobile ITSs extends tutor use to outside of computer labs and traditional classrooms, thereby providing flexible learning opportunities to students at home, after school, and in other locations (Vahey et al, 2004). With the advancement of mobile device technology, there is also the possibility for mobile ITSs to execute as standalone applications, as opposed to client-server network based, thereby eliminating the need for an Internet connection, either wired or wireless.

According to the research conducted by Brown (2009) to determine whether mobile intelligent tutoring system provide learning gains greater than standard instructional activities, it was found out that students using the tutoring condition did experience an increase in post-test performance greater than students that did not use the tutor (using paper and pencil). As a result, it can be concluded that a mobile ITS can provide learning gains greater than standard instruction.

6. Related Work

In the early 1970s a few researchers defined a new goal for computer-based instruction. They adopted the human tutor as their educational model and sought to apply artificial intelligence techniques to realize this model in "intelligent" computer- based instruction.

Personal human tutors provide a highly efficient learning environment (Cohen and Kulik, 1982) and have been estimated to increase mean achievement outcomes by as much as two Intelligent Tutoring Systems standard deviations (Bloom, 1984). The goal of ITSs would be to engage the students in sustained reasoning activity and to interact with the student based on a deep understanding of the students' behavior.

From 1990s, research on pedagogy in the mathematics recognized that students learn mathematics more effectively, if the traditional learning of formulas and procedures is supplemented with the possibility to explore a broad range of problems and problem situations through ITS (Schoenfeld, 1990). In particular, the international comparative study of mathematics teaching (Baumert et al., 1997), has shown that teaching with an orientation towards active problem solving yields better learning results in the sense that the acquired knowledge is more readily available and applicable especially in new contexts and that a reflection about the problem solving activities and methods yields a deeper understanding and better performance.

According to James and Sowmya (2006), Carnegie Learning developed a suite of ITSbased "cognitive tutors" in secondary-level mathematics. The systems, based on earlier research carried out by John Anderson and Ken Koedinger at Carnegie Mellon University, were tested in selected secondary school and students showed 50- to 100-percent improvement in problem solving and use of equations, tables, and graphs.

Eric and Jorg (2003) developed ActiveMath ITS used in problem solving, rule-based systems, knowledge representation, user modeling, adaptive systems and adaptive hyper-media, and diagnosis.

ALEKS (Assessment and Learning in Knowledge Spaces) is an online ITS aimed at tutoring Geometry and Business Mathematics courses (Anderson, Reder, Simon, 1996). It is web based and thus requires Internet connection for it to be accessed.

MathITS (Korhan, 2006) is an Intelligent Tutoring System for mathematics education at undergraduate and graduate level and employs the conceptual map modeling technique (Hwang, 2003). It is a student-centred system, which supports interactive learning.

7. Challenges Faced in Developing Mobile ITS Applications for Mathematics Tutoring

It is easier said than done for instructors, school administrators, and even parents to view mobile devices as being useful for educational purposes because they have been predominately used for social purposes including phone communication and text messaging. The current educational system produces lesson plans, learning activities, and assessments based upon traditional educational models. However, the introduction of mobile devices enables students to interact and collaborate with one another in ways not previously realized. Therefore, instructors must now determine how to design lessons and activities structured around this mobility and accurately quantify the results of the use of the technology.

The use of mobile devices also raises questions that relate to the implementation of the technology, namely the hardware and software. Previous trials of mobile learning applications reveal that concerns regarding device ownership, battery life, and network connectivity can greatly affect the learning outcomes of students (Facer, Faux, and McFarlane, 2005). While these issues may be viewed by some as policy rather than research, it can be argued that an understanding of these issues could provide information to inform the design of the applications themselves. For example, knowing that students may not have reliable Internet connections may cause a designer to create a standalone application or one that requires periodic synchronization for it to function properly.

Interestingly, researchers implementing and testing mobile learning applications have noted that there is potential for mobile learning applications to exist alongside traditional instructional tools (Vahey et al., 2004). While the use of mobile learning applications can be transformative, it is necessary to understand and consider the existing learning environment in which it is intended. While there are certainly instances in which a mobile learning application can provide an experience not possible without the technology (Chen, Kao and Sheu, 2003), it seems reasonable, and even likely, that this technology can co-exist and support traditional paper-based methods.

Representation of diagrams and limited amount of text poses a challenge. As a result, the instructors should decide on which content could best be presented in mobile device. The diagrams representation is limited by screen size.

8. Conclusion

Mobile ITS implementation will help to improve mathematics performance in Kenya Secondary schools. However, certain research areas such as its development, legislation issues, interface, teaching and learning strategies and architecture (hardware and software) should be addressed in order to realize the benefits of mobile ITS. By so doing, Kenya will boast of m-Learning and thus will reach more students helping to bridge the digital divide gap.