Dimitrios Stamovlasis

Conceptual understanding versus algorithmic problem solving: Further evidence from a national chemistry examination

Following our previous paper (Chem. Educator, 2004, 9, 398-405), we analyze further the results of a national examination from the perspective of conceptual learning versus algorithmic problem solving. Detailed achievement data were studied for a sample of 499 eleventh-grade students (age about 17), who were following various branches or streams leading to all kinds of higher-education studies in Greece (the ”Positive‘, the ”Theoretical‘, and the ”Technological‘ Branches). Using qualitative criteria, we distinguished the questions into: (i) simple knowledge-recall, (ii) conceptual, and (iii) well-practiced (algorithmic), stoichiometric, exercises. The latter could further be divided into simple and more demanding ones. As in the previous study, this categorization was also supported by statistical principal component analysis, but this time a marginal structure was extracted, because (possibly) of the limited number and the low difficulty of the postulated conceptual questions. The interest of the study lies mainly in the comparison among the different branches, with the students of the Positive Branch demonstrating the highest mean scores. In addition, students‘ thinking was categorized according to Nakhleh‘s scheme. The Positive Branch had the highest number of students with algorithmic and with conceptual ability, but all branches had about equal share of students high only in conceptual ability. [Chem. Educ. Res. Pract., 2005, 6 (2), 104-118]

The Effect of Three Cognitive Variables on Students’ Understanding of the Particulate Nature of Matter and its Changes of State

In this study, students’ understanding of the structure of matter and its changes of state such as melting, evaporation, boiling, and condensation was investigated in relation to three cognitive variables: logical thinking (LTh), field dependence/independence, and convergence/divergence dimension. The study took place in Greece with the participation of 329 ninth‐grade junior high school pupils (age 14–15). A stepwise multiple regression analysis revealed that all of the above‐mentioned cognitive variables were statistically significant predictors of the students’ achievement. Among the three predictors, LTh was found to be the most dominant. In addition, students’ understanding of the structure of matter, along with the cognitive variables, was shown to have an effect on their understanding of the changes of states and on their competence to interpret these physical changes. Path analyses were implemented to depict these effects. Moreover, a theoretical analysis is provided that associates LTh and cognitive styles with the nature of mental tasks involved when learning the material concerning the particulate nature of matter and its changes of state. Implications for science education are also discussed.

Primary Teachers’ Particle Ideas and Explanations of Physical Phenomena: Effect of an in‐service training course

This paper presents a study concerning Greek primary school teachers’ (n = 162) ideas about the particulate nature of matter and their explanations of physical phenomena. The study took place during an in‐service training course where the effectiveness of a specially designed intervention was tested. A key feature was an approach based on the concept of a substance and its states rather than “solids, liquids, and gases”. Pre‐intervention, the teachers held misconceptions similar to those of pupils. Also, there seemed to be some relationship between the teachers’ particle model ideas and their explanations of phenomena. Post‐intervention, the teachers’ descriptions and explanations were found to be significantly improved, with almost zero correlation between pre‐ and post‐intervention scores. Implications for science education are discussed.

NON-LINEAR ANALYSIS OF THE EFFECT OF WORKING-MEMORY CAPACITY ON ORGANIC-SYNTHESIS PROBLEM SOLVING

This work examines the role of working memory capacity in problem solving in chemistry, and in particular it re-examines the validity of the Johnstone-El Banna predictive model, by employing non-linear methods. The study correlates the students’ information-processing capacity with their performance, by using fractal geometry adapted for treating problem-solving data. The rank order of the subjects’ achievement scores and their working-memory capacities were treated as dynamic flows and found to possess different geometric characteristics depending on the complexity of the problem and the method of marking. The classification and interpretation of these characteristics were made using concepts from complexity theory, such as correlation exponents, fractal dimensions and entropy. The findings support the hypothesis that long-range correlations exist between the rank order of the subjects’ achievement scores and their working-memory capacity, and are in agreement with the Johnstone-El Banna model. [Chem. Educ. Res. Pract. Eur.: 2000, 1, 375-380]

The Role of Goal Orientations in Explaining Academic Cheating in Students With Learning Disabilities: An Application of the Cusp Catastrophe

The purpose of the present study was to predict and explain the academic cheating behaviors of elementary school students with learning disabilities (LD) by applying the cusp catastrophe model. Participants were 32 students with identified LD from state governmental agencies although all both them and the typical students participated in the experimental manipulation (N = 480). Academic cheating was assessed using an empirical paradigm where true achievement was subtracted from achievement in a test without proper invigilation. Data analysis supported the proposed cusp catastrophe models, where mastery-related motives acted as asymmetry and performance goals as bifurcation variables respectively. These findings were confirmed with application of the interactive goal hypothesis (Barron & Harackiewicz, 2001), where the interactive approach and avoidance performance goal term functioned as a splitting factor in the relationship between adaptive motivation and performance.

Primary Teachers’ Understanding of Four Chemical Phenomena: Effect of an In-Service Training Course

One hundred and thirty Greek primary school teachers participated in a study, where the effectiveness of a specially designed intervention on chemical changes was tested. The study took place in the wider context of an in-service training course where the key feature was an innovative approach based on the concept of a substance and its transformations, physical and chemical. In the present paper the focus is on the chemical transformations of substances. Pre-intervention, teachers were found to have a relatively limited ability in explaining chemical changes, which depends on the characteristics of the particular change, and they held a number of misconceptions similar to those of pupils. Post-intervention, teachers’ descriptions and explanations were found to be significantly improved. Also, a relationship between teachers’ particle ideas and their explanations was found. Implications for science education are also discussed.

Methodological and epistemological issues on linear regression applied to psychometric variables in problem solving: rethinking variance

The aim of the present paper is two-fold. First, it attempts to support previous findings on the role of some psychometric variables, such as, M-capacity, the degree of field dependence-independence, logical thinking and the mobility-fixity dimension, on students’ achievement in chemistry problem solving. Second, the paper aims to raise some methodological and epistemological issues concerning the implementation of the general linear model (GLM) in this type of research. Multiple regression analysis was used to analyze the data, which were taken from students (N =86) in tenth grade of high school taking a compulsory course in chemistry. Three different techniques were implemented in order to support a linear model: The Added Variable Plots, the Stepwise Regression and the Best Subsets Regression. Residual analysis and collinearity diagnosis were also performed in order to test the robustness of inferential statistics. The GLM explained 39% of the variance and suggested that only M-capacity and logical thinking were the significant predictors, even though all the correlation coefficients with achievement were statistically significant. The extensive analysis of the linear regression procedures revealed their advantages and also their limitations in terms of statistical robustness. Moreover, a discussion is initiated concerning the explanatory power of linear models and suggests rethinking variance explained under a different philosophical perspective. It is argued that the weakness of the GLM in studying complex dynamical processes, such as problem solving, is rooted not merely in the statistical assumptions that do not hold, or in the variables that are ignored, but substantially it is deeply epistemological.

Reading Achievement, Mastery, and Performance Goal Structures Among Students With Learning Disabilities A Nonlinear Perspective

The purpose of the present study was to examine the hypothesis that a nonlinear relationship exists between a performance-classroom climate and the reading achievement of adolescent students with learning disabilities (LD). Participants were 62 students with LD (Grades 5–9) from public elementary schools in northern Greece. Classroom climate was assessed using the Patterns of Adaptive Learning Styles. Achievement in reading was assessed using a normative reading assessment. Data were analyzed by means of catastrophe theory in which the behavior is predicted as a function of two control variables, the asymmetry factor and the bifurcation factor. Reading achievement (word identification) was predicted by students’ ability to decode pseudowords (asymmetry variable) and by a mastery or performance motivational discourse (bifurcation factor). Results indicated that in classrooms with a performance goal structure, the cusp model fit the data and accounted for 54% of the variance in real word identification. In this condition, the association between pseudoword reading and real word reading was nonlinear. When a mastery climate was tested as a bifurcation variable, results indicated that its effect was nonsignificant and that instead the linear model fitted the data more adequately. Thus, increases in a classroom’s performance motivational discourse are associated with sudden, unpredictable, and discontinued changes in students’ reading performance.

Nonlinear Analysis of the Effect of Working Memory Capacity on Student Performance in Problem Solving

We describe a nonlinear approach for studying the effect of working-memory capacity on science problem solving. The tools used are those of complexity theory and are used to test and model the phenomenon of the working-memory overload. The nonlinear method correlates the subjects’ rank-order achievement scores in problem solving with the psychometric variable. From the achievement scores, rank-order sequences of the subjects, according to their scores, were generated, and in the place of each subject, his/her score was then replaced by the value of the cognitive variable. Then the sequence was mapped onto a one-dimensional random-walk model (working-memory random walk) and treated as dynamical flow. For these sequences, the nonlinear correlation exponents (Hurst exponents) were calculated. The null hypothesis was tested with surrogate data. The method is demonstrated with data from achievement scores in chemical equilibrium problems. Problems of various logical structures and mental demands were used. In problems with simpler logical structure and with low mental demand, the Hurst exponent was close to the surrogate value (demonstrating randomness), while sudden increases appeared for more complicated problems (indicating scale invariant long-range correlations). The method provides meaningful results and adds to the understanding of information processing within the frame of science education.

Nonlinear Dynamical Interaction Patterns in Collaborative Groups: Discourse Analysis with Orbital Decomposition

Social learning theories, contrary to traditional teacher-centered approaches, emphasize social interaction in educational settings, which is the hypothesized driving force in promoting learning. Methodologically, studies of the interaction processes in question often include recording qualities from students’ discourses (e.g., utterances) or other variables measured at the nominal level and examining the distributional features of them, which are potentially associated with learning outcomes. This chapter discusses and illustrates the use of nonlinear framework (NDS) by implementing the orbital decomposition analysis (ODA) when investigating the interaction processes in learning-in-groups approach. Data analysis from unstructured setting, where students freely interact with each other, demonstrates the nonlinear dynamical nature of the underlying processes and reveals how some initial conditions, the unfolding patterns of social interaction, along with individual characteristics might affect the outcomes. OD is a time series analysis of categorical data, which estimates the optimal length of recurrent patterns and calculates nonlinear measures of the time series, such as Shannon entropy, topological entropy, fractal dimension, and Lyapunov exponents. The NDS analysis suggests that the process under investigation could be a complex pattern possessing thresholds and bifurcations, behavior unseen by traditional methods and the black-box approach. Finally, in this chapter an epistemological discussion is provided, which addresses the connection of the social interaction patterns at a microlevel and the fulfillment of the fundamental aims of educational settings anticipated at the macro-level.