Helping students help themselves: Generative learning strategies improve middle school students' self-regulation in a cognitive tutor

The current study investigated whether prompting students to engage in generative learning strategies improves students' subsequent judgments of learning and self-regulation. Seventy-eight middle school students in a pre-algebra class completed worksheets in between problem-solving sessions in a computer-based cognitive tutor. Some students were prompted to engage in a generative learning strategy (i.e., writing a summary or writing an explanation for a peer) followed by a judgment of learning (generative group), whereas other students were only asked to make a judgment of learning (control group). Results indicated non-significant levels of judgment accuracy in both groups; however, students in the generative group showed better-calibrated help-seeking behaviors when solving subsequent problems in the tutor. These results suggest that self-regulation can improve in the absence of accurate learning judgments, and that generative learning strategies can facilitate such an improvement. This may be especially true for younger students, who generally demonstrate lower metacognitive awareness.     

Incremental water charging in agriculture. A case study of the Regione Emilia Romagna in Italy

This paper explores the role that incremental water charging (temporary levies on water use) can play in reducing the environmental costs that arise during drought events. The paper combines a multi-attribute Revealed Preference Model calibrated at a local level (Agricultural District) and a regionally-calibrated Computable General Equilibrium model to estimate the impacts of incremental water charging on water use, water withdrawals and market income. The methodology is applied to the particular case of Italy's Emilia Romagna Region. Results provide a basis for the assessment of tradeoffs in water conservation.     

Contraction theory on Riemannian manifolds

Contraction theory is a methodology for assessing the stability of trajectories of a dynamical system with respect to one another. In this work, we present the fundamental results of contraction theory in an intrinsic, coordinate-free setting, with the presentation highlighting the underlying geometric foundation of contraction theory and the resulting stability properties. We provide coordinate-free proofs of the main results for autonomous vector fields, and clarify the assumptions under which the results hold. We state and prove several interesting corollaries to the main result, study cascade and feedback interconnections of contracting systems, study some simple examples, and highlight how contraction theory has arisen independently in other scientific disciplines. We conclude by illustrating the developed theory for the case of gradient dynamics.     

Just-in-Time-Learning based Extended Prediction Self-Adaptive Control for batch processes

This article presents a new Extended Prediction Self-Adaptive Control (EPSAC) algorithm based on the Just-in-Time Learning (JITL) method. In the proposed JITL-based EPSAC design, linearization of the process model is achieved by a set of local state-space models, each of which can be independently and simultaneously identified by the JITL method along the base trajectory. For the end-product quality control for a simulated semi-batch pH-shift reactive crystallization process where shrinking prediction and control horizons are essential, the proposed EPSAC algorithm not only simplifies the control weight tuning but also provides better and more robust closed-loop control performance than its previous counterpart.     

OpenGnSys: A novel system toward centralized deployment and management of computer laboratories

Operation and status of computers in teaching laboratories can significantly influence motivation and expectation of students, thus resulting in a reduction of their academic progress. To address this issue, this paper presents a complete deployment, management, and maintenance tool for operating systems (OS) called OpenGnSys. As part of a larger educational project, OpenGnSys has been tested in a real computer lab with the aim of improving teaching and learning in classroom. OpenGnSys provides an easy and intuitive way to administrate labs from a graphical user console with remote backup functions. Its design comprises a two-node high availability cluster for both hardware infrastructure and computer services, thus ensuring the availability of essential lab equipment. Additionally, this paper proposes a methodology for the installation, maintenance and quality control for computers and laboratory equipment by site acceptance tests (SATs). The main interest of the proposed SATs lies in representing a standardized procedure, which intends to facilitate and determine the degree of compliance with lab specification and operation services. In order to address the research problem, we analyzed OpenGnSys including lab technicians, 94 students and 7 teachers on Computer Science along three academic years. As a result, the OpenGnSys management system has demonstrated the effectiveness of the followed methodology and positively influenced the students' academic progress.     

Learning to write in an online writing center: The effect of learning styles on the writing process

One of the main advantages of online learning materials is that they can be adapted for students with different learning styles. This article presents a study and a methodology to investigate whether students with different learning styles make use of the potential flexibility of online learning materials, i.c. in the context of an online writing center. The study aims to investigate the effect of learning styles on (a) the students' approach to the writing task (process), and (b) on the letters they write (product). Twenty students each completed a module on writing ‘bad news’ letters designed for Business Communication courses. Their reading and writing processes were recorded. The letters were also graded to determine their quality. An effect of learning style was found: Active and Reflective writers approached the task differently, but only in the beginning of the process. In this early stage Reflective learners were more likely to focus on the theory section than Active learners. This suggests that writers with different learning styles tackle the learning materials in different ways, often in line with the preferences that characterize their learning styles. However, no effect of learning style on text quality was found.     

Digital learners and the overlapping of their personal and educational digital engagement

Current K-12 students are considered digital learners because technology is as pervasive in their academic world as in their personal lives. Technology enthusiasts argue that these learners are the “digital natives” having sophisticated technology knowledge and skills that can be potentially harnessed for better learning engagement inside the classroom. This phenomenological study investigates how some current high school students as the digital learners engage with technology at home and school; and how these two types of engagement overlap in their learning inside the classroom. Data were gathered from phenomenological three series in-depth interviews with five participants and also field observation. Findings show that the overlap between the personal and educational digital engagement(s) of these students was not necessarily positive as portrayed by the prevalent discourses of technology enthusiasts. The overlapping had mixed roles – facilitative as well as obstructive. Pedagogical and future research implications are discussed.     

Scaffolding game-based learning: Impact on learning achievements,perceived learning,and game experiences

One of the central challenges of integrating game-based learning in school settings is helping learners make the connections between the knowledge learned in the game and the knowledge learned at school, while maintaining a high level of engagement with game narrative and gameplay. The current study evaluated the effect of supplementing a business simulation game with an external conceptual scaffold, which introduces formal knowledge representations, on learners' ability to solve financial-mathematical word problems following the game, and on learners' perceptions regarding learning, flow, and enjoyment in the game. Participants (M_age = 10.10 years) were randomly assigned to three experimental conditions: a “study and play” condition that presented the scaffold first and then the game, a “play and study” condition, and a “play only” condition. Although no significant gains in problem-solving were found following the intervention, learners who studied with the external scaffold before the game performed significantly better in the post-game problem-solving assessment. Adding the external scaffold before the game reduced learners' perceived learning. However, the scaffold did not have a negative impact on reported flow and enjoyment. Flow was found to significantly predict perceived learning and enjoyment. Yet, perceived learning and enjoyment did not predict problem-solving and flow directly predicted problem solving only in the “play and study” condition. We suggest that presenting the scaffold may have “problematized” learners' understandings of the game by connecting them to disciplinary knowledge. Implications for the design of scaffolds for game-based learning are discussed.     

一种新的超球SVDD增量学习方法

在基于支持向量数据描述(SVDD)的故障诊断中,往往随着故障数据的不断增加而不断地进行再训练以调整诊断模型,浪费了大量时间。为了解决这一问题,提出一种新的SVDD增量学习算法。该方法在深入分析训练结果与数据样本的关系,多次利用KKT条件,对样本进行筛选,最终选择出影响最终结果的少量训练样本。通过实际电路故障提取采集数据并诊断,所得结果表明该算法可以选择出所有影响结果的相关样本,保证了准确率并避免了大量样本训练,节省了时间。     

Permutation flowshop problems with bi-criterion makespan and total completion time objective and position-weighted learning effects

In this paper, we study the problem of minimizing the weighted sum of makespan and total completion time in a permutation flowshop where the processing times are supposed to vary according to learning effects. The processing time of a job is a function of the sum of the logarithms of the processing times of the jobs already processed and its position in the sequence. We present heuristic algorithms, which are modified from the optimal schedules for the corresponding single machine scheduling problem and analyze their worst-case error bound. We also adopt an existing algorithm as well as a branch-and-bound algorithm for the general m-machine permutation flowshop problem. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems.