STUDENTS' LEARNING DEMAND
Students' visits at a learning center are observable and measurable information about students learning needs. Students are visiting the learning centers for some form of learning assistance; therefore, in the study presented here, the learning demand, measured by the time-distribution of students' visits at a learning center, is assumed to be proportional to the zone of proximal development in terms of Vygotsky.
The time-distributions of students' visits at a learning center are presented as 3D maps of students visits, and the patterns on the maps are used to interpret the fine structure and dynamics of students' learning demand. The integration of computers in education provides opportunities for addressing the individual's learning needs. Using technology as a tool for designing and implementing new learning methods, is presented and briefly discussed.
For educational purposes the most important concept is what part of students' learning demand requires assistance outside of the classroom and what part of the learning demand could be addressed in the classroom. In this paper a 3D visualizations of students visits in a learning center are used to investigate the fine structure (texture) of learning demand for a particular group of students. Statistical methods used by modern physics to study the texture of materials are applied in the study to investigate the texture of students' learning demand. Students learning demand is a hidden parameter. One way to measure indirectly the learning demand is by tracing students' visit at a learning center. The exact day, time during the day and duration of students' visits is recorded in the front-desk database of each modern learning center. Each student's visit is considered an event of the learning demand for a particular group of students. The learning demand for each group is a distribution with two independent variables - day and time during the day. The dependent variable is student's time investment. For each student, the length of the visit is the individual learning demand for this day and time. All students visiting a learning center form a population for this study.
Part of the learning demand measured by students' visits at the learning center requires assistance and part of the learning demand requires adjustment of the teaching methodology or light guidance. Sometimes students are visiting learning centers to study independently. However, this is learning assistance in a larger sense of this word. Providing a supportive learning environment is an important part of the modern learning services. Even as a stand alone service, maintaining learning environment is as important as assistance by the tutors. The learning demand is complex and dynamic characteristic of total learning needs of individuals.
Students' Learning Demand and the Zone of Proximal Development
The zone of proximal development concept is used in this study as a guiding theory. Lev Vygotsky has an elegant and simple concept about the zone of proximal development: "the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance (tutoring) or in collaboration with more capable peers" (Vygotsky, 1978, p. 86 in Cheyne and Tarulli, 1999).
Let assume that the learning demand is observable part of the zone of proximal development. One possible way to assist students' performance in the zone of proximal development is to scaffold the task for students and to guide them to the predefined destination as it is required by the curriculum (Bodrova and Leong, 1995, 1998). The observations of this study are conducted without any assumptions on the individual's zone of proximal development; however, the statistical patterns of the observations should describe the shape and dynamics of these proximal areas for a group of students. The new approach used in the study is to observe the total picture of the learning demand for a group of students and to find out if there are some patterns that will help to develop adaptive services for students' learning demand on the level of individuals. The problem is researched in three aspects: the relation between learning demand and the zone of proximal development, the relation between learning demand and individual learning preferences, and description of a long-term multipurpose workshop as one way to address the findings of the first two parts of the study.
Part one is build upon students learning demand for three courses of developmental mathematics. The study shows that the demand for learning assistance decreases in the learning process although the intellectual challenge is increasing as students advanced from arithmetic to college algebra. This finding is in agreement with Bodrova and Leong's research on the zone of proximal development (1998). The same pattern of learning demand distribution may be valid all technologies. A melting zone of proximal development phenomenon is not as evident as it might seem. One contradiction to this phenomenon is the common sense opinion that some students are good in math and some are not. Mathematics is an old tool for natural sciences and has many practical applications. How to integrate a tool named mathematics, which is not easy, with a tool called computers? The answer to this question requires knowledge of students' personal learning preferences.
Students' Learning Demand and Learning Orientations
In the second part of this study, the fine texture of students' learning preferences is discussed as it acts upon and shapes the learning demand. The problem of students' learning preferences is researched with a Learning Orientations Questionnaire (Copyright © 1997-2001, Margaret Martinez). Knowing students learning orientations is a one way to find the individuals' impact on learning demand. This part of the study provides information about some patterns in the learning demand due to students' Learning Orientations. The problem of learning demand doesn't become any easier, but with knowledge of learning orientation for a group of students, some relative patterns for the group provide information about the methods of teaching and assisting students. The next step is to accept the fact that students will need assistance outside of the classroom to learn how to use technology as a multipurpose tool. Therefore, it is beneficial to join the effort to assist students with the regular teaching practices in the classroom. Two almost disconnected educational practices of building technological infrastructure and classroom teaching should be coordinated to merge into one new joint effort to assist students' learning. The technology is the key ingredient in customized education, however, the form of using technology is not well researched yet.
To provide one possible practical answer to the question of using technology as a learning tool, a workshop on learning with technologies as a long-term multipurpose learning developed to incorporate the observation of students' learning demand. The workshop is aim to slow the learning process with technology integration in order to decompose, or defreeze the routine learning and to observe and analyze the elements with immediate feedback, reflections and improvement. With other words, the workshop is designed to provide learning environment for the so called "action research".
Population
With few exceptions, all students are low achieving on placement test. The target population for this study consists of adult students enrolled in developmental classes in a community college . All students visiting the learning center for support in mathematics form a population for part one of this study. The observation at the learning center shows that even passing the low level math classes and moving in the higher level classes, the students still need tutoring in the basics (the problem with memorization and information reprocessing). The dynamic of tutoring process depends on the day of semester (curricular dependence), and students' personality profiles. The personality profile is studied with 51 students. The learning level of independence for this group is less than 4% compared with 25% for university students (Martinez, 2003).
Method
To observe stable patterns of students' learning demand, a front desk check-in and check-out data is used to trace the patterns and tendencies.
The open-door policy at a Learning Center is used to select naturally a population for this research. Each student, enrolled in developmental mathematics classes has equal right to visit the center for assistance by tutors or for co-curricular learning services. The sample is actually a population of all students, enrolled in developmental classes, who are willing to invest time for additional learning services. The time-investment is recorded on a daily basis at the front-desk database. This time investment is used as a dynamical measure of students' learning demand. The results depend on curricular and teaching methods. However, the free access to the learning center and statistical character of the students' learning demand could be captured day after day and used as a manifestation of the learning needs of one particular population. The study presents students' choices by a map of the total time at any moment of the day-time space.
Research Questions
Three questions are considered in this study:
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Provided that students' free will to use learning services in a learning center is proportional to their learning demand for each subject matter, the first question is how this learning demand is changing in the process of learning with increasing intellectual challenges?
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If there is way to measure or quantify the impact of students' learning preferences, how could researchers investigate the dynamic of students' learning demand as it depends on students' personality profiles?
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Using the fine structure of students' learning demand, how to apply all findings of this study by integrating technologies as a new learning tools and intellectual challenge along with old curriculum objectives? (one practical solution)
Concepts
To answer the research questions listed above, two main concepts are used in this study:
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Students' learning demand measured by the distribution of time invested by the students in a learning center.
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Students' Learning Orientations measured by students' answers to the Learning Orientation Questionnaire (courtesy Dr. M. Martinez, 2005)
References
Bodrova, E., & Leong, D. (1995). Tools of the Mind: The Vigotskian Approach to Early Childhood _______Education. New York: Merill/Prentice-Hall.
Bodrova, E., & Leong, D. (1998). Scaffolding Emergent Writing in the Zone of Proximal ______Development. Literacy Teaching and Learning, 3(2), 1.
Cheyne, A. J., & Tarulli, D. (1999). Dialogue, Difference, and the "Third Voice" in the Zone of ______Proximal Development. Theory and Psychology, 9, 5-28.
Cheyne, A. J., & Tarulli, D. (2005). Dialogue, Difference, and the "Third Voice" in the Zone of ______Proximal Development. Retrieved July 22, 2005, from University of Waterloo, Waterloo, ______Ontario, Canada: http://watarts.uwaterloo.ca/~acheyne/ZPD.html
Martinez, M.. Supporting Individual Learning Differences. Retrieved February 19, 2005, from The _______Training Place: http://www.trainingplace.com/source/research/customization.htm
Papert, Y. (1996). A Word for Learning. In Constructionism in Practice: Designing, Thinking, and _______Learning in a Digital world (pp. 9-24). Mahwah, New Jersey: Lawrence Erlbaum_Associates, _______Publishers.
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Last Updated - 11/19/2005