Instructing students on effective sequences of examples and problems: Does self-regulated learning improve from knowing what works and why?

Nowadays, students often practice problem-solving skills in online learning environments with the help of examples and problems. This requires them to self-regulate their learning. It is questionable how novices self-regulate their learning from examples and problems and whether they need support. The present study investigated the open questions (1) to what extent students' (novices) task selections align with instructional design principles and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem-solving procedure by fixed sequences of examples and problems (FS-condition), or by self-regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL-condition) or without (SRL-condition) watching a video detailing the instructional design principles. Students' task-selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL-condition than in the SRL-condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively ‘safe’ to allow students to independently start learning new problems-solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow-up research should investigate how we can further improve self-regulated learning from examples and practice problems.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

A Generalization of Jumps and Labels

This paper describes a new language feature that is a hybrid of labels and procedures. It is closely related to jumping out of a functional subroutine, and includes conventional labels and jumping as a special, but probably not most useful, case. It is independent of assignment, ie., it can be added to a purely-functional (non-imperative) system (such as LISP without pseudo-functions or program feature). Experiments in purely functional programming suggest that its main use will be in success/failure situations, and failure actions. This innovation is incorporated in the projected experimental system, ISWIM.     

Regional patterns of affirmative action compliance costs

In this paper we estimate the amount by which firms' labor costs are increased by compliance with affirmative action regulations imposed on federal contractors for 160 SMSAs in 1980. The paper focuses on determining the cause(s) for variation in these compliance costs across SMSAs and in identifying areas of high and low compliance cost. We find that compliance costs are lower in larger SMSAs, more rapidly growing SMSAs, and SMSAs that have relatively large proportions of minorities and females in the labor market.The authors would like to thank Fred Wildes and Long Gen Ying, both of San Diego State University, for their assistance on this project. In addition, support for Prof. Getis' work was provided by the National Science Foundation, Grant No. SES-9123832.This paper was presented at the annual conference of the Western Regional Science Association in Tucson, Arizona.     

Intrinsic plagiarism analysis

Research in automatic text plagiarism detection focuses on algorithms that compare suspicious documents against a collection of reference documents. Recent approaches perform well in identifying copied or modified foreign sections, but they assume a closed world where a reference collection is given. This article investigates the question whether plagiarism can be detected by a computer program if no reference can be provided, e.g., if the foreign sections stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism analysis; it is closely related to the problem of authorship verification. Our contributions are threefold. (1) We organize the algorithmic building blocks for intrinsic plagiarism analysis and authorship verification and survey the state of the art. (2) We show how the meta learning approach of Koppel and Schler, termed “unmasking”, can be employed to post-process unreliable stylometric analysis results. (3) We operationalize and evaluate an analysis chain that combines document chunking, style model computation, one-class classification, and meta learning.     

On-column solvent exchange for purified preparative fractions

On-column solvent exchange, using many of the principles of solid-phase extraction, has been implemented to significantly reduce evaporation cycle time following reverse-phase preparative HPLC. Additional benefits, such as a reduced potential for salt formation, thermal decomposition, and residual solvent, are also described. Fractions obtained from preparative separations, typically in a large volume of acetonitrile:water, are injected into the preparative HPLC and then eluted in acetonitrile, creating a new fraction in a volatile organic solvent. Minimal modification to the instrument was required, and unattended operation is possible. Acetonitrile evaporation is achieved within 3 h, compared with 17 h for aqueous-based fractions; lower temperatures can be used during the evaporation step; mobile-phase additives, likely to form salts with the target compound if concentrated in the fraction, are removed before evaporation; sample recovery and purity are unaffected.     

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model [T. Preis et al., Journal of Chemical Physics 228 (2009) 4468-4477] in order to overcome the memory limitations of a single GPU which enables us to simulate significantly larger systems. Using multi-spin coding techniques, we are able to accelerate simulations on a single GPU by factors up to 35 compared to an optimized single Central Processor Unit (CPU) core implementation which employs multi-spin coding. By combining the Compute Unified Device Architecture (CUDA) with the Message Parsing Interface (MPI) on the CPU level, a single Ising lattice can be updated by a cluster of GPUs in parallel. For large systems, the computation time scales nearly linearly with the number of GPUs used. As proof of concept we reproduce the critical temperature of the 2D Ising model using finite size scaling techniques.     

Whole body vibration exposures in forklift operators: comparison of a mechanical and air suspension seat

Using a repeated measures design, this study compared differences in whole body vibration (WBV) exposures when 12 forklift operators drove the same forklift with a mechanical suspension and an air suspension seat. A portable PDA-based WBV data acquisition system collected and analysed time-weighted and raw WBV data per ISO 2631-1 and 2631-5 WBV measurement standards. Tri-axial measurements of weighted vibration (A(w)), crest factor, vibration dose values, time-weighted average-peak, raw (+) peak, raw (-) peak and static compression dose (S(ed)) were compared between seats. There were significant differences in z-axis WBV exposures with the air suspension seat, yielding lower WBV exposures. In addition, there were differences between seats in how they attenuated WBV exposures based on the driver's weight. In the mechanical suspension seat, WBV exposures were weight-dependent, with lighter drivers having higher WBV exposures, whereas with the air suspension seat, the same trends were not as prevalent. STATEMENT OF RELEVANCE: This study contributes to the understanding of how different seat suspensions can influence WBV transmission and how some components of vibration transmission are dependent on the weight of the driver. Additional systematic studies are needed to quantify how various factors can influence WBV exposures.     

Personalized text snippet extraction using statistical language models

In knowledge discovery in a text database, extracting and returning a subset of information highly relevant to a user's query is a critical task. In a broader sense, this is essentially identification of certain personalized patterns that drives such applications as Web search engine construction, customized text summarization and automated question answering. A related problem of text snippet extraction has been previously studied in information retrieval. In these studies, common strategies for extracting and presenting text snippets to meet user needs either process document fragments that have been delimitated a priori or use a sliding window of a fixed size to highlight the results. In this work, we argue that text snippet extraction can be generalized if the user's intention is better utilized. It overcomes the rigidness of existing approaches by dynamically returning more flexible start-end positions of text snippets, which are also semantically more coherent. This is achieved by constructing and using statistical language models which effectively capture the commonalities between a document and the user intention. Experiments indicate that our proposed solutions provide effective personalized information extraction services.