A Diagnostic Framework for the Empirical Evaluation of Learning Maps

Learning progressions and learning map structures are increasingly being used as the basis for the design of large-scale assessments. Of critical importance to these designs is the validity of the map structure used to build the assessments. Most commonly, evidence for the validity of a map structure comes from procedural evidence gathered during the learning map creation process (e.g., research literature, external reviews). However, it is also important to provide support for the validity of the map structure with empirical evidence by using data gathered from the assessment. In this paper, we propose a framework for the empirical validation of learning maps and progressions using diagnostic classification models. Three methods are proposed within this framework that provide different levels of model assumptions and types of inferences. The framework is then applied to the Dynamic Learning Maps® alternate assessment system to illustrate the utility and limitations of each method. Results show that each of the proposed methods have some limitations, but they are able to provide complementary information for the evaluation of the proposed structure of content standards (Essential Elements) in the Dynamic Learning Maps assessment.

Walnut Ripeness Detection Based on Coupling Information and Lightweight YOLOv4

To realize rapid and accurate ripeness detection for walnut on mobile terminals such as mobile phones, we propose a method based on coupling information and lightweight YOLOv4. First, we collected 50 walnuts at each ripeness (Unripe, Mid-ripe, Ripe, Over-ripe) to determine the kernel oil content. Pearson correlation analysis and one-way analysis of variance (ANOVA) prove that the division of walnut ripeness reflects the change in kernel oil content. It is feasible to estimate the kernel oil content by detecting the ripeness of walnut. Next, we achieve ripeness detection based on lightweight YOLOv4. We adopt MobileNetV3 as the backbone feature extractor and adopt depthwise separable convolution to replace the traditional convolution. We design a parallel convolution structure with depthwise convolution stacking (PCSDCS) to reduce parameters and improve feature extraction ability. To enhance the model’s detection ability for walnuts in the growth-intensive areas, we design a Gaussian Soft DIoU non-maximum suppression (GSDIoU-NMS) algorithm. The dataset used for model optimization contains 3600 images, of which 2880 images in the training set, 320 images in the validation set, and 400 images in the test set. We adopt a multi-training strategy based on dynamic learning rate and transfer learning to get training weights. The lightweight YOLOv4 model achieves 94.05%, 90.72%, 88.30%, 76.92 FPS, and 38.14 MB in mean average precision, precision, recall, average detection speed, and weight capacity, respectively. Compared with the Faster R-CNN model, EfficientDet-D1 model, YOLOv3 model, and YOLOv4 model, the lightweight YOLOv4 model improves 8.77%, 4.84%, 5.43%, and 0.06% in mean average precision, 74.60 FPS, 55.60 FPS, 38.83 FPS, and 46.63 FPS in detection speed, respectively. And the lightweight YOLOv4 is 84.4% smaller than the original YOLOv4 model in terms of weight capacity. This paper provides a theoretical reference for the rapid ripeness detection of walnut and exploration for the model’s lightweight.

The Effectiveness of Gallery Run Technique on Saudi EFL Upper-Intermediate Students’ Speaking Skill: Learners’ Attitudes in Focus

Gallery Run (GR) is a classroom-based dynamic learning technique that promotes higher-order thinking and co-operative learning. This research study examined the influence of GR strategy on Saudi upper-intermediate English as a foreign language(EFL) learners’ oral skills. First off, 62 upper-intermediate EFL learners from a language school in Riyadh were opted and randomly sectioned into an experimental group and a control group. Then, the participants in the two groups were given a speaking pretest. The experiment group applied the GR technique in their classroom, whereas the control group received no treatment and continued with an ordinary classroom program. After two-month classes, a speaking posttest was given to the two groups. To analyze the data collected, Independent and Paired Samples T-tests were conducted. The results showed that the experimental group excelled and outperformed the control group. The results also showed that 25.8% and 48.3% of the participants agreed and strongly agreed respectively that the GR technique enhanced their self-dependence. Furthermore, 45% of the participants reported that the ambiance was delightful, which conduced to the amelioration of their speaking competence. Another significant result was that 61.2% and 19.3% of the learners strongly agreed and agreed respectively that GR reduced the levels of loneliness and social anxiety. Furthermore, 58.1% strongly agreed that the GR technique did not put them under any kind of stress, nor did it encourage social loafing. A final finding reported by the 48.4 % of the learners was that GR reduced their alexithymia, social anxiety, and self-monitoring.

Constructions of Artistic Creativity in the Letters of Vincent van Gogh

Artistic creativity is presently considered to be a multidimensional phenomenon that unfolds over time and is in constant conversation with the social and historical context of the artists, as well as their personal life experiences. This article adopts a narrative perspective and explores Vincent van Gogh’s understanding of the constructs of creativity as reflected in his letters to his brother Theo, friends, and other family members. To inquire into van Gogh’s correspondence, narrative thematic analysis was employed. Findings highlight the artist’s constructs around creativity, which seem to depict elements of both modern and post-modern views of creativity. Major themes include creativity as (a) a developmental, dynamic learning process characterized by dedication and persistence; (b) a relational process in the context of people and nature; (c) an embodied action; (d) an oscillation between asceticism and socio-cultural participation, (e) suffering, and (f) a larger-than-life force. With this study, we join the conversation of scholars around recent developments in the field of creativity, calling for a variety of perspectives and methodological approaches to this complex and multifaceted construct. Moreover, we hope to move beyond the ‘mad genius’ stereotype and myths around psychopathology and artistic creativity, as exemplified in the present analysis of van Gogh’s correspondence.

The Moderator of Innovation Culture and the Mediator of Realized Absorptive Capacity in Enhancing Organizations' Absorptive Capacity for SPI Success

Software process improvement (SPI) is critical to information system development. In the context of successful SPI, this research focuses on a firm's dynamic learning ability to see how it facilitates an effective means of acquiring and utilizing external SPI knowledge in responding to changing software development environments. Specifically, the authors propose a research model to investigate how two mechanisms of absorptive capacity are incorporated with innovation culture as a contextual factor to enable successful software process improvement. A survey was conducted including 125 SPI certified firms in China and Taiwan to examine the model. The findings indicate that a firm's potential absorptive capacity significantly influences realized absorptive capacity, which has a significant impact on SPI success and acts as a partial mediator between potential absorptive capacity and SPI success. Moreover, the results suggest that the mediating effect of potential absorptive capacity on SPI success via realized absorptive capacity is amplified when innovation culture is imposed.

The Effectiveness of Gallery Run Technique on Saudi EFL Upper-Intermediate Students’ Speaking Skill: Learners’ Attitudes in Focus

Gallery Run (GR) is a classroom-based dynamic learning technique that promotes higher-order thinking and co-operative learning. This research study examined the influence of GR strategy on Saudi upper-intermediate English as a foreign language(EFL) learners’ oral skills. First off, 62 upper-intermediate EFL learners from a language school in Riyadh were opted and randomly sectioned into an experimental group and a control group. Then, the participants in the two groups were given a speaking pretest. The experiment group applied the GR technique in their classroom, whereas the control group received no treatment and continued with an ordinary classroom program. After two-month classes, a speaking posttest was given to the two groups. To analyze the data collected, Independent and Paired Samples T-tests were conducted. The results showed that the experimental group excelled and outperformed the control group. The results also showed that 25.8% and 48.3% of the participants agreed and strongly agreed respectively that the GR technique enhanced their self-dependence. Furthermore, 45% of the participants reported that the ambiance was delightful, which conduced to the amelioration of their speaking competence. Another significant result was that 61.2% and 19.3% of the learners strongly agreed and agreed respectively that GR reduced the levels of loneliness and social anxiety. Furthermore, 58.1% strongly agreed that the GR technique did not put them under any kind of stress, nor did it encourage social loafing. A final finding reported by the 48.4 % of the learners was that GR reduced their alexithymia, social anxiety, and self-monitoring.

Thermodynamic Machine Learning through Maximum Work Production

Adaptive systems---such as a biological organism gaining survival advantage, an autonomous robot executing a functional task, or a motor protein transporting intracellular nutrients---must model the regularities and stochasticity in their environments to take full advantage of thermodynamic resources. Analogously, but in a purely computational realm, machine learning algorithms estimate models to capture predictable structure and identify irrelevant noise in training data. This happens through optimization of performance metrics, such as model likelihood. If physically implemented, is there a sense in which computational models estimated through machine learning are physically preferred? We introduce the thermodynamic principle that work production is the most relevant performance metric for an adaptive physical agent and compare the results to the maximum-likelihood principle that guides machine learning. Within the class of physical agents that most efficiently harvest energy from their environment, we demonstrate that an efficient agent's model explicitly determines its architecture and how much useful work it harvests from the environment. We then show that selecting the maximum-work agent for given environmental data corresponds to finding the maximum-likelihood model. This establishes an equivalence between nonequilibrium thermodynamics and dynamic learning. In this way, work maximization emerges as an organizing principle that underlies learning in adaptive thermodynamic systems.