An Overview of Intelligent Tutoring System Authoring Tools, Research Paper Example

Intelligent Tutoring Systems ITS bolsters complex problem-solving skills for students through strategy freedom. However, they are architecturally complex and thus are difficult to construct and helps cultivate in students the capacity to ascertain various solution strategies for any problem. The question arises, however, if granting students more freedom in problem solving enhances the learning process. Waalkens et al, (2013) developed three different types of the same ITS used to solve linear algebraic equations that varied solely in how much freedom students had. One stipulation mandated that students adhere to the standard strategy, while the other two had both minor and major variations. The study was carried out in two different middle schools in the United States with a sample of fifty seven students that were in 7th and 8th grades. As a whole, the algebra skills of the students showed improvement, and the experiment disproved the hypothesis that the degree of leverage students had did not impact learning outcomes or innate motivation. The authors note that students had a propensity to use the standard strategy, with some showing minor variations. Therefore, this study indicates that the ITS should enable students to have a degree of freedom in the preliminary stages of learning problem-solving within a complex matrix. In order for students to develop a degree of flexibility in their problem-solving and strategizing processes, the argues not that there is more needed than merely allowing students to devise their own strategy for problem solving.

Based on extant literature and studies, Waalkens et al. (2013) hypothesized that stricter tutors help students learn about problem-solving strategies while “freer” tutors help students acquire deeper knowledge and transfer learning. Students who problem solve in stricter conditions purportedly would score higher on familiar equations such as those present in ITS. This study made several meaningful contributions to studies on learning outcomes in various conditions. This was the first study to probe the educational currency off granting students strategy freedom while they were problem-solving with an ITS. Only minor strategy variations enhanced outcomes by minimizing the frequency of “valid strategy variations marked wrong by the system” (Waalkens et al., 2013). Another contribution is that the tutor log data provides great insight into the strategies students use to solve algebraic equations, as this study shows that students use a limited range of problem-solving strategies. A final contribution is that it explores an avenue of an issue in education that has hitherto been largely ignored with regards to the role of advanced learning technologies such as ITS. This study lays the foundation for researchers to better enhance strategic flexibility in order to produce better learning outcomes. Although this premise remains peripheral to the current study, the capacity to pinpoint students based on their strategy use has the potential to establish a causal relationship between the use of experimental intervention and on the use of strategy. This would enable researchers to identify differences on an idiosyncratic basis regarding how the participate responds to the intervention.

The methodology was well-thought out, although the sample of participants was too small for it to be representative on a national level. Because this study is the first of its kind, there is room for adjustments in order to more comprehensively assess the range of problem solving with ITS. The pre-test in this current study was very small due to a lack of time to be more comprehensive. Ultimately, it is unequivocal that strategic flexibility is an integral component for math learning and proficiency. To improve learning, it is clear that a measure of strategy needs to be integrated by learning technologies. Waalkens et al. (2013) concluded that students failed to take advantage of the flexibility allowed by the interface. If students are not told to solve problems utilizing a vast array of strategies, then it was unlikely that a student would opt to use a divergent strategy. Building on this study, future researchers need to actively promote the utilization of divergent strategies in order to actually observe and quantify the strategic flexibility of students. Moreover, this study used more simplistic modeling strategies, so researchers need to develop more sophisticated methodologies and detection mechanisms  for detecting how they are deployed. Such developments and changes to research on strategic flexibility and ITS will enable researchers to further investigate how strategically-focused interventions impact a student’s capacity to solve complex math problems in the future.

The currency of this study lies in the fact that it paves the way for more research about the efficacy of ITS and other interfaces that will enable students to use a variety of strategy methods to solve complex math problems. ITS technologies need to be built in order to measure and propel students to utilize flexible strategy. As such, future studies should not confine models into particular contexts but rather extend them to those in which students enjoy more freedom to make various transformations within one step. This facet was evident in this study. It would have added nuance to this study had the authors explored different types of interfaces that could cultivate strategic flexibility to varying degrees. Researchers indeed still need to better understand how

References

Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring  goes to school in the big city. International Journal of Artificial Intelligence in Education, 8(1), 30–43.

Murray, T. (2003). An overview of intelligent tutoring system authoring tools: updated analysis of the state of the art. In T. Murray, S. Blessing, & S. Ainsworth (Eds.), Authoring tools for advanced learning environments (pp. 491–544). Dordrecht, the Netherlands: Kluwer Academic Publishers

Waalkens, M., Alevan, V, & Taatgen,N. (2013). Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems. Computers & Education, 60, 159-171.