Positive and negative effect in intelligent tutoring system using mathematica
Intelligent system of tutoring is a computer system used to give instant, customized feedback or instructions to students without the help of a human teacher or instructor. Mathematica is software used to carry out symbolic and numerical computations. This software is used in intelligent tutoring systems to give students direction when conducting mathematics related studies or research. The use of mathematica in intelligence tutoring has various advantages as well as disadvantages.
Some of the merits of using mathematica include; the software has been used widely in intelligence tutoring successfully. Many top scientists and researchers from all disciplines of science all over the world prefer to use mathematica because it is effective and gives accurate results. The application has been preferred over others like Math Lab because of its ability to carry out both symbolic and numerical computations.
Mathematica is also efficient intelligent tutoring because it has in built scientific functions. This makes the application convenient to use. Scientists can carry out multiple scientific activities with mathematica because of mathematica’s versatility. The application has also been identified by most researchers as one of the most powerful and accomplished visualization engines in the industry. It has built in visualization effects, which make it possible for researchers to produce quality work. This property also benefits those scientists with optical problems t use the application comfortably because of distinct visuals.
Mathematica can be used to write and read a wide range of file formats without having to use extra coding. No other application used in intelligent tutoring can read as many files as mathematica. Therefore, scientists and researchers can use material from different kinds of files, and they also have the option of writing their work in any file format that they prefer. Finally, mathematica is efficient in intelligent tutoring because it helps researchers validate their hypotheses. The procedure of validating a hypothesis is usually cumbersome, especially when dealing with both numerical and symbolic values in the study. Mathematica provides a shortcut to validating a hypothesis because of its inbuilt scientific functions.
However, mathematica has some demerits too. The application is very expensive hence cannot be afforded by some students and researchers. People like college students carry out similarly complicated research using intelligent tutoring. Since they cannot afford efficient applications like mathematica, they end up using poor applications, which affect the validity of their findings.
Using mathematica in intelligent tutoring has been proven to be very slow compared to other applications like Maple. The application uses so many codes which slow down the process of tutoring. Therefore, it is prone to result in time wasting. This problem can be solved if the researcher uses multiple computers and a set of compiled codes. However, this is cumbersome and requires a lot of finance to purchase several computers just for one researcher or student.
The use of mathematica in intelligent tutoring is too convenient that it results in negative effects on students and researchers. Once students get used to having everything done by the application, they are prone to laxity. Their ability in programming and using other computer languages drops significantly because they do not exercise often. This can be avoided by teaching learners procedural implementation before introducing them to its processing equivalent.
Therefore, using mathematica in intelligent tutoring is very advantageous because it is effective and helps deliver accurate results. This is because of its inbuilt visual engine, scientific functions and ability to read and write different file formats without using extra coding. Its disadvantages include its slow nature and its high costs of purchase. Furthermore, its convenience affects the student’s ability to program.
References
Nkambou, R., Mizoguchi, R., & Bourdeau, J. (2010). Advances in Intelligent Tutoring Systems (illustrated ed.). Munich: Springer.
Rose, C., & Smith, M. D. (2002). Mathematical Statistics With Mathematica (illustrated ed.). Munich: Springer.
Ruskeepää, H. (2009). Mathematica Navigator: Mathematics, Statistics, and Graphics (3, illustrated ed.). New York: Academic Press.
Stankov, S., Glavinic, V., & Rosic, M. (2010). Intelligent Tutoring Systems in E-Learning Environments: Design, Implementation, and Evaluation (illustrated ed.). New York: IGI Global Snippet.
Trott, M. (2004). The Mathematica GuideBook for Programming (illustrated, annotated ed.). Munich: Springer.
