Effect of Cueing on Learning Transfer Among Pre-professional Undergraduate Healthcare Students Engaged in a Case-based Analogical Reasoning Exercise

To examine the extent of transfer of cued versus non-cued pre- professional healthcare undergraduates engaged in a case-based analogical reasoning exercise. Independent t-test analysis and effect size was calculated to assess transfer between cued and non-cued participants (N = 192). Cued participants (n = 98, M = 2.30, SD = .89) demonstrated significantly more transfer (t (175.91) = 2.65; p = .009; CI95 = (.10, 0.68); d = .39) than non-cued participants (n = 94, M = 1.9, SD = 1.14). Learning transfer improves among pre- professional undergraduates when cued during a case-based analogical reasoning experience

MS

thesisAs case-based learning (CBL) via computer-assisted instruction becomes a burgeoning instructional method within medical education, its pedagogical value must be ascertained. In particular, the relative efficacy of specific instructional elements that comprise the CBL methodology must be determined. For example, numerous laboratory studies have proposed conditions that may facilitate knowledge transfer via analogy (case-based reasoning). However, few of these methods have been evaluated in complex learning environments such as medicine. The study included in this thesis, The Medulator™ Analogical Reasoning Study"" (MARS), employed an online patient simulation application to evaluate several potential methods for the optimization of learning by clinical novices (i.e., medical students). Medulator™, a commercial Web-based patient simulation application, was modified to test the effects of case sequencing, explicit case comparison, and user-generated case summaries on user performance. Senior medical students self-enrolled via the Internet and were randomized to complete analogous sets of virtual patient cases in different sequences, with or without an explicit analogical reasoning exercise being invoked for analogous case pairs and with or without the ability to generate user-authored case summaries. Specific aspects of their case performance were then tracked. A brief follow-up user survey was conducted to determine overall satisfaction with the online CBL approach and to determine perceived value of the analogical reasoning component. A significant effect of case sequencing on analogy transfer was seen only with respect to correct treatment scores (p = .009). Explicit case comparison had no reliable effect on performance. However, diagnostic accuracy increased (p = .002) while treatment attempts decreased (p = .05) when subjects were prompted to write case summaries. The additional time needed to write case summaries was not statistically significant (p = .12). Overall, user satisfaction with the Medulator™ was excellent. However, high perceived value of the analogical reasoning component was not supported by measured results. Manipulating case sequences and supporting explicit case comparison yielded mixed results, suggesting that these methods afford instructional value only under specific learning conditions. However, using case summaries as a tool for reflection and proxy for self-explanation led to significant early and sustained improvement in students' performance."

On-line case-based policy learning for automated planning in probabilistic environments

Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope.This paper has been partially supported by the Spanish Ministerio de Econom a y Competitividad TIN2015-65686-C5-1-R and the European Union's Horizon 2020 Research and Innovation programme under Grant Agreement No. 730086 (ERGO)

Knowledge transfer in cognitive systems theory: models, computation, and explanation

Knowledge transfer in cognitive systems can be explicated in terms of structure mapping and control. The structure of an effective model enables adaptive control for the system's intended domain of application. Knowledge is transferred by a system when control of a new domain is enabled by mapping the structure of a previously effective model. I advocate for a model-based view of computation which recognizes effective structure mapping at a low level. Artificial neural network systems are furthermore viewed as model-based, where effective models are learned through feedback. Thus, many of the most popular artificial neural network systems are best understood in light of the cybernetic tradition as error-controlled regulators. Knowledge transfer with pre-trained networks (transfer learning) can, when automated like other machine learning methods, be seen as an advancement towards artificial general intelligence. I argue this is convincing because it is akin to automating a general systems methodology of knowledge transfer in scientific reasoning. Analogical reasoning is typical in such a methodology, and some accounts view analogical cognition as the core of cognition which provides adaptive benefits through efficient knowledge transfer. I then discuss one modern example of analogical reasoning in physics, and how an extended Bayesian view might model confirmation given a structural mapping between two systems. In light of my account of knowledge transfer, I finally assess the case of quantum-like models in cognition, and whether the transfer of quantum principles is appropriate. I conclude by throwing my support behind a general systems philosophy of science framework which emphasizes the importance of structure, and which rejects a controversial view of scientific explanation in favor of a view of explanation as enabling control

Promoting future teachers' evidence-informed reasoning scripts: effects of different forms of instruction after problem-solving

Pre-service teachers face difficulties when dealing with problem situations in the classroom if their evidence-informed reasoning script (EIRS) is not adequately developed. An EIRS might be promoted by demonstrating how to implement evidence-informed reasoning after a problem-solving activity on an authentic case. However, it is unclear what form of instruction is appropriate to promote pre-service teachers in the development of an EIRS. The present 2×3-factorial experimental intervention study investigated how different forms of instruction on functional procedures (example-free vs. example-based) and on dysfunctional procedures (without vs. example-free vs. example-based) affect the development of an EIRS. N = 384 pre-service teachers worked on a written case vignette of a problem situation in a problem-solving phase, in which the crucial steps of the EIRS were prompted externally. In the subsequent instruction phase, the participants compared their own solution with an example-free or example-based instruction on functional procedures, which was either supplemented by an example-free or example-based instruction on typical dysfunctional procedures or not at all. The participants’ learning success (declarative EIRS; near and far transfer problem-solving performance) and error awareness were assessed. The results revealed that the example-based instruction on functional procedures led to a higher learning success than the example-free instruction. Both forms of instruction on dysfunctional procedures improved learning success compared to learning without one. During learning, error awareness was higher for learners who worked with an example-free instruction on dysfunctional procedures. In order to promote the development of an EIRS in pre-service teachers, it is promising to provide instruction after problem-solving that presents a functional example of evidence-informed reasoning for the given problem and that also points out typical dysfunctional approaches to solving the problem. The results highlight the importance of selecting appropriate scaffolds in case-based learning approaches that aim to develop cognitive schemata. The mechanisms that explain when and why instructions on dysfunctional procedures work need to be further explored

Case studies of cycles in developing a physics lesson.

Children\u27s reasoning and learning about levers and simple machines were investigated in this study. The study included several cycles of design, test and clinical interview tutoring sessions and the two final cycles are presented here. The methodology combined the use of qualitative clinical interviewing data and quantitative summative data: quantitative evaluations provided an overview of the lessons\u27 effects, while qualitative, formative lesson evaluations allowed deeper insights into learning and reasoning processes. Three groups of participants were interviewed about the pretest, lesson and posttest. The pre- and posttests were standardized, and several new and widespread misconceptions about levers have been discovered that are less accurate or general than conventional conceptions. In experiment 1 the pre-posttest comparison between the control group and experimental group 1 showed that there were no differences and the instruction in experiment 2 was revised considerably as a result of the formative evaluation findings. Significant improvements were apparent for experimental group 2 with regard to conceptual change and for transfer when compared with experimental group 1--evident in group 2 students\u27 ability to transfer their acquired knowledge to complex and compound levers and in conceptual changes apparent in simple levers questions. Lesson 1 was essentially a bridging lesson where intuitive anchoring examples were extended analogically via intermediate bridging cases to a target situation. The findings from lesson 1 suggested that reasoning from extreme case situations of levers might be instructionally useful, and this hypothesis was confirmed by results from experiment 2, where the instructional sequences based on extreme case reasoning proved to be powerful facilitators of the construction of mechanistic models by the students that fostered conceptual change and learning. The following directions for further research are suggested: students\u27 conceptual models have implications for teaching and learning that are poorly understood at this stage, and research on instruction that employs experts\u27 non-formal reasoning strategies should be encouraged

Lehetőségek és kihívások a digitális játék alapú tanulásban: egy induktív gondolkodást fejlesztő program hatásvizsgálata

The aim of this study is to investigate the effectiveness of an online training game which develops inductive reasoning strategies through tasks embedded in mathematical content. Participants were 314 primary school children in Years 3 and 4. Participants (N=88) received a five-week-long computer-based training (20- to 40-min. sessions) in the school’s ICT room. The control group was matched based on pre-test scores, year and gender. Klauer’s model of inductive reasoning and his concept of Cognitive training for children (Klauer, 1989) were applied to develop the training program. The online game consisted of 120 learning tasks with varied embedded mathematical content (e.g. recognizing and discriminating relations or attributes through mathematical operations, number series or units of measurement). In order to enhance learning, immediate feedback and, in the case of failure, instructional support were provided for every learning task. An inductive reasoning test was used to assess the near transfer effect of the training (43 figural, non-verbal items, Cronbach’s α=.86). Children’s attitudes toward the game were measured with five-point-scale questionnaire items after the training. The Electronic Diagnostic Assessment System (eDia) was used in order to deliver both the assessment and the training material. The experimental group scored significantly higher on the post-test (t(174)=-2.288, p=.02). The effectiveness of the program proved to be unrelated to gender (t(86)=-.520, p=.60 or year t(86)=-.425, p=.67). The effect size of the training program was d=.33. Children reported that they enjoyed playing the game and had positive attitudes towards it. Further analyses of the data revealed that not every child improved during the training and that two of the inductive strategies did not develop significantly at the group level. Due to the inherent assessment techniques in the game, incorrectly functioning learning tasks can be identified empirically in order to further develop the tasks generally. Our findings demonstrate an example of how to integrate mathematical content and reasoning strategies into a digital game-based learning environment. It is recommended that further studies should investigate the long-term transfer effect of the training and the influence of additional game elements (e.g. game story) on learning achievement

Similarity and explanation for dynamic telecommunication engineer support.

Understanding similarity between different examples is a crucial aspect of Case-Based Reasoning (CBR) systems, but learning representations optimised for similarity comparisons can be difficult. CBR systems typically rely on separate algorithms to learn representations for cases and to compare those representations, as symbolised by the vocabulary and similarity knowledge containers respectively. Deep Metric Learners (DMLs) are a branch of deep learning architectures which learn a representation optimised for similarity comparison by leveraging direct case comparisons during training. In this thesis we explore the symbiotic relationship between these two fields of research. Firstly we examine what can be learned from traditional CBR research to improve the training of DMLs through training strategies. We then examine how DMLs can fill the traditionally separate roles of the vocabulary and similarity knowledge containers. We perform this exploration on the real-world problem of experience transfer between experts and non-experts on service provisioning for telecommunication organisations. This problem is also revealing about the requirements for practical applications to be explainable to their intended user group. With that in mind, we conclude this thesis with work towards the development of an explanation framework designed to explain the recommendations of similarity-based classifiers. We support this practical contribution with an exploration of similarity knowledge to support autonomous measurement of explanation quality

Transfer Learning for Improving Model Predictions in Highly Configurable Software

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'17