Students Learning Styles - Essay Example

?Statistics Education and IT Introduction Christou and Dinov conducted a research en d, “A Study Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education.” Both being part of the Statistics Online Computational Resource (SOCR) of the University of California, Los Angeles (UCLA), they have adequate educational backgrounds and work experiences to come up with the said endeavor. In the cited text, the authors investigate two research questions: (1) whether there is a significant evidence that technology enhanced instruction facilitates knowledge retention, boosts motivation and improves student satisfaction; and (2) whether there are learning-style specific effects that may influence the quantitative outcomes of traditional or IT-enhanced instruction (p. 547). With rapid advancements of and in technology, many fields have incorporated the Internet and its vast possibilities in their development. For instance, SOCR is an evidence of statistics utilizing online applications, programs, et cetera, to be a resource center for individuals interested with the field, wherever they may be. Because of the emergence of various tools in the integration of science, technology, and education, it is not impossible to have information technology (IT) methods of instruction. As a matter of fact, many academic institutions have provided online courses and programs in their curriculum. Statistics may be difficult for some students, and having options for education other than the traditional methods may have the possibility of facilitating easier learning and comprehension in the subject matter. The methods, results, and conclusions of the study will be discussed in this paper, as well as an analysis of the endeavor, looking into its strengths and weaknesses, including recommendations and implications for future researches. Review and Analysis I. Methods The researchers utilized “SOCR resources as instruments for IT-blended instruction in several courses,” where these courses included quizzes at the beginning of the quarter, Index of Learning Styles (ILS) assessment, standard quarter-wide learning evaluation quantitative measures through exams, quizzes, and homework, and beginning and ending attitude towards the subject surveys (p. 548). The design of the study is seen to gather both quantitative and qualitative data and is briefly described in the methodology portion of the paper, but one could notice that specific design traits are presented with the results, and in their corresponding statistical courses. The total number of subjects was not mentioned, but a portion in the paper states that there were 90 students involved in each section. Several variables were measured, including an assessment of background knowledge on the first day of classes, learning styles, exam scores, pre- and post-attitude surveys, and satisfaction surveys at the end of the quarter. The setting for research conduction was not specified, but probably took place in statistics classes of UCLA. II. Results and Conclusions Outcomes of the study confirm gathered results of a previous study that indeed there is “technology-driven improvement of the quantitative performance in probability and statistics courses” (p. 557). In addition, findings suggest that learning styles and attitudes of students towards a discipline are important factors in their quantitative performance. The SOCR-treatment group shows a tendency in the increase of student satisfaction according to post-surveys, and standard assessment instruments revealed that there was quantitative performance improvement. Researchers thus conclude that complementing pedagogical methods with information technology positively affects the students’ academic performance and attitudes. With the use of technology-based instructional tools, students are able to understand and comprehend statistical concepts that are difficult to do so in the sole use of traditional classroom approaches. III. Strengths and limitations The paper is thoroughly written, and results show in detail what happened in the course of the study. The research purpose and inquiries presented in the early part of the article are met as well. One strength identified in the study is that its design allowed only two instructors, wherein they were each given both control and treatment groups to teach. This strategy lessened the contradiction of results, and added reliability that exams, assessments, et cetera, given to the groups are comparable. Although there may be posed speculations for a tendency of bias because the instructors are insider-researchers at the same time, it should be noted that this inclination was not allowed to potentiate in the study, as can be seen through their methods. In addition, qualitative researches, or those with a touch of qualitative approach, are more often than not, advantaged by insider-researchers because they are able to witness changes in reactions and performance better than outsider-researchers. Further, the researchers provided links where course descriptions of the classes and tools utilized in their endeavor can be accessed, allowing readers to check for validity and reliability of data. Despite these, however, it is mentioned that the ILS and attitude surveys were only available for the treatment group, specifically for the Fall 2006 class (p. 550). It could have been expressed why the control group was not assessed in these measures, or if these were not needed or appropriate in the non-treatment subjects. Some readers could contend that there may be attitude and learning styles that the control group possesses that could affect the results, either positively or negatively. IV. Recommendations The research by Christou and Dinov (2010) undeniably expose that the use of information technology in integration with traditional teaching strategies could benefit students, specifically in the field of statistics. It is known that statistics sometimes include confusing concepts, and in these cases, visuals, charts, interactive applications, et cetera, brought about by modern technology could make comprehension easier to students. It is thus recommended that academic institutions adapt this system in their curricula in statistics, and possibly venture the same strategy in other courses as well. To students, such study may encourage them to access online resources, such as SOCR, to complement what they learn in class, especially if their institution still solely uses the pedagogical approach to learning. Conclusion Christou and Dinov (2010) have provided evidences that students’ attitudes, learning styles, and knowledge acquisition in statistical education can be enhanced with the integration of traditional and IT-based instruction. The research design and methods, as mentioned, are thoroughly presented to support their propositions. In Statistics, the findings of this study implies that there be more online resources to facilitate increased learning, and other disciplines could also take the present study as a model to integrate IT-based tools into their fields. Reference Christou, N., & Dinov, I. D. (2010). A study of students’ learning styles, discipline attitudes and knowledge acquisition in technology-enhanced probability and statistics education. MERLOT Journal of Online Learning and Teaching, 6 (3). Read More