The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

Innovative learning in action (ILIA) issue four: New academics engaging with action research

This edition of ILIA showcases four papers which were originally submitted as action research projects on the Postgraduate Certificate in Higher Education Practice and Research programme. Within the programme we offer an environment where participants can explore their unique teaching situations – not to produce all-encompassing approaches to Higher Education (HE) practice but to develop an ongoing dialogue about the act of teaching. In effect, there are no generalisable ‘best’ methods of teaching because they never work as well as ‘locally produced practice in action’ (Kincheloe, 2003:15). Thus rather than providing short term ‘survival kits’ the programme offers new HE teachers a ‘frame’ for examining their own and their colleagues’ teaching alongside questioning educational purpose and values in the pursuit of pedagogical improvement. This ‘frame’ is action research which Ebbutt (1985:156) describes as: …The systematic study of attempts to change and improve educational practice by groups of participants by means of their own practical actions and by means of their own reflections upon the effects of their actions… We promote ‘practitioner-research’ or ‘teacher-research’ as a way of facilitating professional development for new HE teachers, promoting change and giving a voice to their developing personal and professional knowledge. Teachers as researchers embark upon an action orientated, iterative and collaborative process to interrogate their own practices, question their own assumptions, attitudes, values and beliefs in order to better understand, influence and enrich the context of their own situations. The action researcher assumes that practitioners are knowledgeable about their own teaching situations and the fact that they are ‘in-situ’ and not at ‘arms length’ as the value-neutral, ‘scientific’ researcher is often claimed to be, does not invalidate their knowledge. Thus, practitioners are capable of analysing their own actions within a ‘reflective practitioner’ modus operandi. Action research is on-going in conception and well suited to examining the ever-changing and increasingly complex HE practice environment. Findings from action research are always subject to revision since it intrinsically acknowledges the need to constantly revisit widely diverse teaching situations and scenarios across everyday HE practice. Teaching is not predictable and constant, it always occurs in a contemporary microcosm of uncertainty. Action research provides an analytical framework for new HE teachers to begin to engage with this unpredictability on a continuing basis, that is its purpose and also its perennial challenge. The papers presented here describe how four relatively new HE teachers have begun to address the challenge of improving their practice within their locally based settings utilising the action research ‘paradigm’

Law Talk: Speaking, Writing, and Entering the Discourse of Law

The author suggests talking about the legal writing process with first-year legal research and writing students, as they are learning and actively writing, and advocates for students\u27 experiencing being the audience of legal writing, as part of their education. This Article reviews three academic schools of thought regarding the relationship between speech and writing. This Article argues for change in the typical legal writing pedagogy, meaning more student interaction and teacher intervention, to effectively enable students to engage in discourse communities of law

Cognitive development in relation to science education

Various skills have been considered quintessential to the scientific method. The need for these skills was highlighted by Armstrong at the beginning of the century and continues to be re-iterated to the present day within the criteria of the National Curriculum. Pupils as scientists are expected to make accurate and meaningful observations; record results from experiments formulated to test hypotheses, controlling all the relevant variables except the one under investigation; identify patterns within the results and recognise anomalies; draw valid conclusions from the data collected and extrapolate from the data to predict further results. These criteria were included in the list of thirty-two teacher assessed skills in domains five and six of the Northern Examination Association, NEA, GCSE Biology Syllabus. This research project endeavoured to test the acquisition of these skills in a large sample of students drawn from a variety of schools in an effort to establish the relative difficulty of the individual skills. The corollation of performance of the skills with a range of factors, including IQ, the influence of gender, school type, and associated subjects they studied was explored. In particular the effect of an exposure to the Warwick Process Science Scheme was investigated to establish whether a transferable long term enhancement resulted. The main body of the research was undertaken on Year ten (4th Year) pupils, the sample being drawn from ten schools of varying types. The work was extended to include both younger and older age groups, to identify the progress made with age in skill acquisition and to investigate whether success in the skills is of predictive value for the final GCSE grades of future 'A'Level achievement. The results indicated a wide variability in degrees of difficulty of the individual skills and a wide range of performance by individual candidates. Success in the skills corollated very closely with IQ, so to eliminate this effect samples cross-matched for IQ were investigated to establish the effect of other variables. Only the study of the three separate sciences and tuition within a selective school proved to have a significant effect on the outcome. Only skill 30 devising three separate hypotheses to explain a complex set of results, had predictive value for GCSE and none were of value for predicting capital 'A'Level success

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Identification and remediation of student difficulties with quantitative genetics.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.Genetics has been identified as a subject area which many students find difficult to comprehend. The researcher, who is also a lecturer at the University of KwaZulu-Natal, had noted over a number of years that students find the field of quantitative genetics particularly challenging. The aim of this investigation was two-fold. Firstly, during the diagnostic phase of the investigation, to obtain empirical evidence on the nature of difficulties and alternative conceptions that may be experienced by some students in the context of quantitative genetics. Secondly, to develop, implement and assess an intervention during the remediation phase of the study which could address the identified difficulties and alternative conceptions. The research was conducted from a human constructivist perspective using an action research approach. A mixed-method, pragmatic paradigm was employed. The study was conducted at the University of KwaZulu-Natal over four years and involved third-year students studying introductory modules in quantitative genetics. Empirical evidence of students' conceptual frameworks, student difficulties and alternative conceptions was obtained during the diagnostic phase using five research instruments. These included: free-response probes, multiple-choice diagnostic tests, student-generated concept maps, a word association study and student interviews. Data were collected, at the start and completion of the modules, to ascertain the status of students' prior knowledge (prior knowledge concepts), and what they had learnt during the teaching of the module (quantitative genetics concepts). Student-generated concept maps and student interviews were used to determine whether students were able to integrate their knowledge and link key concepts of quantitative genetics. This initial analysis indicated that many students had difficulty integrating their knowledge of variance and heritability, and could not apply their knowledge of quantitative genetics to the solution of practical problems. Multiple-choice diagnostic tests and interviews with selected students were used to gather data on student difficulties and alternative conceptions. The results suggested that students held five primary difficulties or alternative conceptions with respect to prior knowledge concepts: (1) confusion between the terms variation and variance; (2) inappropriate association of heterozygosity with variation in a population; (3) inappropriate association of variation with change; (4) inappropriate association of equilibrium with inbred populations and with values of zero and one; and, (5) difficulty relating descriptive statistics to graphs of a normal distribution. Furthermore, three major difficulties were detected with respect to students understanding of quantitative genetics concepts: (1) students frequently confused individual and population measures such as breeding value and heritability; (2) students confused the terms heritability and inheritance; and, (3) students were not able to link descriptive statistics such as variance and heritability to histograms. Students found the concepts of variance and heritability to be particularly challenging. A synthesis of the results obtained from the diagnostic phase indicated that many of the difficulties and alternative conceptions noted were due to confusion between certain terms and topics and that students had difficulty with the construction and interpretation of histograms. These results were used to develop a model of the possible source of students' difficulties. It was hypothesized and found that the sequence in which concepts are introduced to students at many South African universities could be responsible for difficulties and alternative conceptions identified during the study, particularly the inappropriate association of terms or topics. An intervention was developed to address the identified difficulties and alternative conceptions. This intervention consisted of a series of computer-based tutorials and concept mapping exercises. The intervention was then implemented throughout a third year introductory module in quantitative genetics. The effectiveness of the intervention was assessed using the multiple-choice diagnostic tests and interview protocols developed during the diagnostic phase. The knowledge of the student group who participated in the intervention (test group) was compared against a student group from the previous year that had only been exposed to conventional teaching strategies (control group). t-tests, an analysis of covariance and a regression analysis all indicated that the intervention had been effective. Furthermore, an inductive analysis of the student responses indicted that most students understanding of the concepts of variance, heritability and histograms was greatly improved. The concept maps generated by students during the remediation phase, and data from the student interviews, provided an indication of the nature and extent of the conceptual change which had occurred during the teaching of the module. The results showed that most of the conceptual change could be classified as conceptual development or conceptual capture and not conceptual exchange. Furthermore, it seemed that conceptual change had occurred when considered from an epistemological, ontological and affective perspective, with most students indicating that they felt they had benefited from all aspects of the intervention. The findings of this research strongly suggest an urgent need to redesign quantitative genetics course curricula. Cognisance should be taken of both the sequence and the manner in which key concepts are taught in order to enhance students' understanding of this highly cognitively demanding area of genetics

Use of collaborative computer simulation activities by high school science students learning relative motion.

Galileo\u27s contemporaries as well as today\u27s students have difficulty understanding relative motion. It is hypothesized that construction of visual models, resolution of these visual models with numeric models, and, in many cases, rejection of epistemological commitments such as the belief in one true velocity, are necessary for students to form integrated mental models of relative motion events. To investigate students\u27 relative motion problem solving, high school science students were videotaped in classroom and laboratory settings as they performed collaborative predict-observe-explain activities with relative motion computer simulations. The activities were designed to facilitate conceptual change by challenging common alternative conceptions. Half of the students interacted with simulations that provided animated feedback; the other half received numeric feedback. Learning, as measured by a diagnostic test, occurred following both conditions. There was no statistically significant difference between groups on the measure. It is hypothesized that students did not show statistically significant performance differences on the relative motion test because (a) many students were able to solve numeric problems through algorithm use; (b) many numeric condition students were aided in their ability to visualize problems by interaction with the treatment; and (c) the animation condition fostered little learning because the activities were too easy for students to perform. Students\u27 problem solving was examined through analyses of protocols and through statistical analyses of written responses. Evidence supported the following findings: (1) Numeric condition students had more difficulty with the computer activities than animation condition students. (2) Many students in both groups were able to construct accurate mental models of relative motion events. (3) A number of numeric condition students used faulty mechanical algorithms to solve problems. (4) A number of animation condition students used visualization to solve problems, mapping dynamic visual features of the animations onto posttest problems. Thus, there is evidence that presentation of numeric data can foster students\u27 use of mechanical algorithms. Presentation of animations can foster visualization of target problems solved off-line. These results suggest that, in addition to the structure of the simulations, how computer simulations are used may have a great impact on students\u27 cognition