An assessment on the effect of collaborative groups on students’ problem-solving strategies and abilities

This paper reports the use of tools to probe the effectiveness of using small-group interaction to improve problem solving. We find that most students' problem-solving strategies and abilities can be improved by working in short-term, collaborative groups without any other intervention. This is true even for students who have stabilized on a problem-solving strategy and who have stabilized at a problem-solving ability level. Furthermore, we find that even though most students improve by a factor of about 10% in student ability, there are two exceptions: Female students who are classified as pre-formal on a test of logical thinking improve by almost 20% when paired with concrete students; however if two students at the concrete level are paired together no improvement is seen. It has been said that problem solving is the ultimate goal of education (1), and certainly this is true in any chemistry course (2). To be sure, most instructors value this skill and try to instill the ability to solve problems in their students. However, the term "problem solving" means different things to different audiences, from algorithmic problems to complex, open-ended problems that do not have one particular solution. A number of attempts have been made to define problem solving, including "any goal-directed sequence of cognitive operations" (3), and many now agree with the general definition: "what you do when you don't know what to do" (4). Problem solving can be closely allied to critical thinking (5), that other goal of most science courses, in that it involves the application of knowledge to unfamiliar situations. Problem solving also requires the solver to analyze the situation and make decisions about how to proceed, which critical thinking helps. A number of information processing models for problem solving have been developed (6-8) and attempts made to develop uniform theories of problem solving (9). However, many of these studies involve knowledge-lean, closed problems (2) that do not require any specific content knowledge to solve, and that have a specific path to the answer. The truth is that many types of problems exist and there is not one model that will be effective for all categories (10). For example, in teaching science we are ultimately concerned with knowledge-rich problems requiring scientific content knowledge. Studies on problem solving in chemistry have typically revolved around development of strategies derived from research on closed-ended problems, usually pinpointing areas of difficulty that students encounter in specific subject types, such as stoichiometry or equilibrium. A number of studies where students are given strategies or heuristics allowing them resolve word problems in order to produce a numerical answer by application of an algorithm Open-ended problem solving that requires students to use data to make inferences, or to use critical thinking skills, is much more difficult to incorporate into introductory (and even higher level) courses; it is even more difficult to assess, particularly when large numbers of students are involved. Traditional assessment methods, such as examinations and quizzes-including both short answer and multiple choice-give very little insight into the problem-solving process itself. If a student does not have a successful problem-solving strategy, these methods may not allow either the student or the instructor to see where the difficulty lies, or to find ways to improve. While other investigation methods such as think-aloud protocols and videotaped problem-solving sessions (14) give a more nuanced picture of the problem-solving process (15-17), these techniques are time consuming, expensive, and require specific expertise to analyze. These methods are certainly not applicable for the formative assessment of large numbers of students, and while they give a snapshot of a student's problem-solving ability at the time of observation, it is even more difficult to monitor students' development of problem-solving expertise over an extended period. The upshot of all this previous research is that while we know a great deal about the problem-solving process in an abstract environment, we do not in fact have much insight into how students solve many types of scientific problems. Since we lack this information about how students approach problems and how students achieve competence, it is not easy to address the difficulties that students encounter as they develop problemsolving abilities. Indeed, while instructors value problem-solving skills highly, it is often the case that the only explicit instruction that many students are exposed to is the modeling of the skill as the instructor solves problems for students. So we have a situation where a valued skill is often not fully developed in students, even though we implicitly expect that they will become competent problem solvers by the end of the course. The most common assessments give no real insight into student strategies for problem solving, and therefore there is little feedback the instructor can give in terms of how to improve. The traditional assessments also tend to measure and reward algorithmic problem-solving skills rather than critical thinking and application of knowledge to new situations. It seems clear that if we are serious about wanting to incorporate meaningful problem solving into our courses, then we must go beyond the traditional assessments and design systems that allow us t

Differential Use of Study Approaches by Students of Different Achievement Levels

This study examined similarities and differences in study approaches reported by general chemistry students performing at different achievement levels. The study population consisted of freshmen enrolled in a required year-long general chemistry course at the U.S. Naval Academy. Students in the first and second semesters of the course were surveyed using a modified version of the published Approaches and Study Skills Inventory for Students (ASSIST) referred to as the M-ASSIST (Modified Approaches and Study Skills Inventory). Responses to items associated with using deep or surface approaches to studying were examined for students of three achievement levels (A/B, C, and D/F course grades) using both ANOVA and Structured Means Modeling to look for differences in study approaches between achievement levels. Results show that, with only 12 items, the M-ASSIST can be used to measure differences in reported use of deep and surface approaches by students in different achievement groups; that Structured Means Modeling can uncover significant differences that are not apparent with an ANOVA analysis of the same data; and that A/B and D/F students can be classified as reporting using either using primarily deep (A/B students) or primarily surface (D/F) study approaches. C students reported study approaches characteristic of both the A/B and D/F groups, leading to the interpretation that C students may be in an intermediate and possibly transitional state between the higher- and lower-grade groups. These results suggest a new understanding of C students as those who may not fully implement deep approaches to studying but, in general, demonstrate less reliance on surface approaches than lower-achieving students

Memory CD8+ T cells vary in differentiation phenotype in different persistent virus infections.

The viruses HIV-1, Epstein-Barr virus (EBV), cytomegalovirus (CMV) and hepatitis C virus (HCV) are characterized by the establishment of lifelong infection in the human host, where their replication is thought to be tightly controlled by virus-specific CD8+ T cells. Here we present detailed studies of the differentiation phenotype of these cells, which can be separated into three distinct subsets based on expression of the costimulatory receptors CD28 and CD27. Whereas CD8+ T cells specific for HIV, EBV and HCV exhibit similar characteristics during primary infection, there are significant enrichments at different stages of cellular differentiation in the chronic phase of persistent infection according to the viral specificity, which suggests that distinct memory T-cell populations are established in different virus infections. These findings challenge the current definitions of memory and effector subsets in humans, and suggest that ascribing effector and memory functions to subsets with different differentiation phenotypes is no longer appropriate