Sophisticated Learning Research Articles

Editorial: Beyond Cognitive Ability

The past 70 years of research in learning has primarily favoured a cognitive perspective. As such, learning and learning performance were measured based on factors such as memory, encoding, and retrieval. More sophisticated learning activities, such as perspective changes, still relied on a fundamental cognitive architecture (Dunlosky & Rawson, 2019). Early researchers advocating for a constructivist learning lens, such as Piaget, also assessed development on a range of cognitive tasks. Over the past several decades, this view of learning as cognitive has given rise to a range of augmenting perspectives. Researchers increasingly focus on mindsets, social learning, peer effects, self-regulation, and self-perception to evaluate the broader scope of learning. For learning analytics (LA), this transition has important implications for data collection and analysis, tools and technologies used, research design, and experimentation. This special issue continues existing conversations around LA and emerging competencies (Dawson & Siemens, 2014; Buckingham Shum & Crick, 2016) but also reflects the growing number of researchers engaging with these topics.

‘A not unedifying field for some local antiquary of the future’: new evidence on library activity and mutual improvement. The experience of the north-east of Scotland

Mutual improvement, an early form of lifelong learning, was widespread among the nineteenth-century working classes and has been portrayed as a variable and relatively unstructured phenomenon. This essay challenges this view by examining the movement in north-east Scotland in the nineteenth century and its symbiotic relationship with library activity as libraries provided information to facilitate debate. The movement originated in the 1830s and flourished until the end of the century. Mutual improvement activity was fuelled by religious division and a relationship with the Liberal Party. The principal ideologue of the movement, which peaked in the 1850s, was Robert Harvie Smith, who articulated a sophisticated lifelong learning ideology supported by specific learning objectives, prioritised in order. A notable feature was the involvement of women in the movement. Most of the participants were tradesmen or small tenant farmers, and the subjects of their debates reflected their preoccupations: modern farming, religious controversy, and the ‘farm servant problem’. The movement anticipated the university extension movement by about thirty years. Because the north-east had its own university and was a self-contained learning culture, mutual improvers might proceed to university, thus anticipating modern ideas about received prior learning (RPL) and articulation. Mutual improvement activity demonstrates the continuing intellectual vitality in rural Scotland in the late nineteenth century.

A light tracker for online multiple pedestrian tracking

We propose a novel real-time multiple pedestrian tracker for videos acquired from both static and moving cameras in unconstrained real-world environment. In such scenes, trackers always suffer from noisy detections and frequent occlusions. Existing methods usually use complex learning approaches and a large number of training samples to get discriminative appearance features. However, this leads to high computational cost and hardly works in occlusions (missing detections) and undistinguishable appearance. Addressing this, we design a light two-stage tracker. Firstly, a shallow net with two layers of full convolution is proposed to encode appearance. Compared with other deep architectures and sophisticated learning approaches, our shallow net is efficient and robust enough without any online updating. Secondly, we design a motion model to deal with noisy detections and missing objects caused by motion blur or occlusion. By mining the motion pattern, our tracker can reliably predict the object location under challenging scenes. Furthermore, we propose a speedup version to verify our robustness and the possibility of using in online applications. Extensive experiments are implemented on multiple object tracking benchmarks, MOT15 and MOT17. The performance is competitive over a number of state-of-the-art trackers and demonstrates that our tracker is very promising for real-time applications.

Dynamic Learning Platform for Dynamic EFL Learners: A New Journey to Navigate Effective E-Learning Program for English Education at the University Level

With the innovation of technology, the nature of learning and teaching English as a Foreign Language (EFL) has been changed, and instead of traditional learning and teaching, e-learning has emerged as a new way to meet the demands of the ‘iGeneration’ who can easily roam around the knowledge domain just with a smartphone at anytime from anywhere. This research aims to find out the challenges of e-language learning that will help establish a more sophisticated Virtual Learning Environment (VLE) by implementing the latest version of the Learning Management System (LMS). E-learning is ‘constructive learning’ or ‘self-regulated learning’ where the learners work as ‘knowledge generators’ and teachers as ‘facilitators’ and so, the learning should be accomplished in a platform that ensures interaction, communication, and collaboration. This research will be provocative of establishing such an environment where online foreign language learners and instructors can adapt easily to the new pedagogical approach to learning for successfully attaining the target learning outcomes. A survey was conducted at the department of English, Najran University (Girls’ section), the Kingdom of Saudi Arabia, and the findings show the necessity of improving teachers’ efficiency in selecting e-learning tools to design learning materials and contents of the course considering cultural context and the modern-day learners’ capacity as well as the goal. It also discusses various implications regarding the need for e-language learners’ skill development that vigorously affects the learning process. The paper finally recommends some possible steps that can be adopted by a university for the successful implementation of e-learning to widen the realm of knowledge for the learners of English as Foreign Language (EFL).

An Exact Expectation Model for the LMS Tracking Abilities

Nonstationary environments are ubiquitous in communications and acoustic systems. The ability to track their dynamics is one of the most desirable features of adaptive processing algorithms. The designers of these algorithms employ guidelines derived from stochastic analyses to adjust user-defined parameters to maximize performance or avoid stability issues. It is therefore important that analyzes of adaptive processing algorithms take into account the built-in sophisticated learning capabilities. This work presents a comprehensive model of the performance of the least mean square algorithm, operating under Markovian time-varying channels. Our advanced analysis considers both transient and steady-state regimes. Furthermore, in our analysis, the popular independence assumption is not adopted, resulting in a stochastic model which is accurate even when: (i) the step size is not infinitesimally small; or (ii) when the unknown system presents a high nonstationary degree. In addition, our evaluation is also able to provide a deterministic theoretical step-size sequence that optimizes algorithmic performance, as well as an accurate step size upper bound that guarantees algorithm stability. Computer simulations performed are in accordance with our theoretical predictions.

On the Fusion of Text Detection Results: A Genetic Programming Approach

Hundreds of text detection methods have been proposed, motivated by their widespread use in several applications. Despite the huge progress in the area, which includes even the use of sophisticated learning schemes, ad-hoc post-processing procedures are often employed to improve the text detection rate, by removing both false positives and negatives. Another issue refers to the lack of the use of the complementary views provided by different text detection methods. This paper aims to fill these gaps. We propose the use of a soft computing framework, based on genetic programming (GP), to guide the definition of suitable post-processing procedures through the combination of basic operators, which may be applied to improve detection results provided by multiple methods at the same time. Performed experiments in the widely used ICDAR 2011, ICDAR 2013, and ICDAR 2015 datasets demonstrate that our GP-based approach leads to F1 effectiveness gains up to 5.1 percentage points, when compared to several baselines.

Smarter Features, Simpler Learning?

Earlier work on machine learning for automated reasoning mostly relied on simple, syntactic features combined with sophisticated learning techniques. Using ideas adopted in the software verification community, we propose the investigation of more complex, structural features to learn from. These may be exploited to either learn beneficial strategies for tools, or build a portfolio solver that chooses the most suitable tool for a given problem. We present some ideas for features of term rewrite systems and theorem proving problems.

Physical Human-Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators.

This article presents a state-of-the-art survey on the robotic systems, sensors, actuators, and collaborative strategies for physical human-robot collaboration (pHRC). This article starts with an overview of some robotic systems with cutting-edge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed the relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances is made, some research directions, and future challenges are presented.

The Limits of Infants’ Early Word Learning

ABSTRACTIn this series of experiments, we tested the limits of young infants’ word learning and generalization abilities in light of recent findings reporting sophisticated word learning abilities in the first year of life. Ten-month-old infants were trained with two word-object pairs and tested with either the same or different members of the corresponding categories. In Experiment 1, infants showed successful learning of the word-object associations, when trained and tested with a single exemplar from each category. In Experiment 2, infants were presented with multiple within-category items during training but failed to learn the word-object associations. In Experiment 3, infants were presented with a single exemplar from each category during training, and failed to generalize words to a new category exemplar. However, when infants were trained with items from perceptually and conceptually distinct categories in Experiment 4, they showed weak evidence for generalization of words to novel members of the corresponding categories. It is suggested that word learning in the first year begins as the formation of simple associations between words and objects that become enriched as experience with objects, words and categories accumulates across development.

Learning Deep Landmarks for Imbalanced Classification

We introduce a deep imbalanced learning framework called learning DEep Landmarks in laTent spAce (DELTA). Our work is inspired by the shallow imbalanced learning approaches to rebalance imbalanced samples before feeding them to train a discriminative classifier. Our DELTA advances existing works by introducing the new concept of rebalancing samples in a deeply transformed latent space, where latent points exhibit several desired properties including compactness and separability. In general, DELTA simultaneously conducts feature learning, sample rebalancing, and discriminative learning in a joint, end-to-end framework. The framework is readily integrated with other sophisticated learning concepts including latent points oversampling and ensemble learning. More importantly, DELTA offers the possibility to conduct imbalanced learning with the assistancy of structured feature extractor. We verify the effectiveness of DELTA not only on several benchmark data sets but also on more challenging real-world tasks including click-through-rate (CTR) prediction, multi-class cell type classification, and sentiment analysis with sequential inputs.

Simulation Games: Learning Goal Orientations and Norms for Knowledge Sharing

With advances in technology, simulation games have become commonplace in business education. However, little is known about effective ways to incorporate these sophisticated learning tools into the...

Management Game

Digital disruption and continuous productivity improvement require more from people management, thus raising the bar for leadership competencies. International studies indicate that leadership competence gaps are large and traditional leadership training methods does not seem to solve this problem. This article's findings supports this situation. The authors will open the complexity behind organizational productivity development and present game theoretical architecture that simulates management behavior effects to human performance. New methods enable practice-based learning that enables formatting leaders' behavior so that it will create long-term success with continuous change. The authors will present gamified leadership training procedure and discuss the practical learning experiences from a management simulation game. The authors' study reveals challenges at interactive leadership skills, thus, it is argued, that there seems to be problems at the leadership mind-set. Therefore, more sophisticated learning methods and tools should be used.

The Development of Curriculum in Homeschooling of Primagama Serang

This research is aimed to get deep understanding about the development Curriculum in Homeschooling of Primagama Serang. The research uses qualitative research with case study as the method. Subjects of research are principals, academic staff, teacher, and students of Homeschooling Primagama Serang. Data collecting technique for this research are observation, interview, and documentation. The result of research shows that Homeschooling of Primagama Serang has run its curriculum which integrated to 2013 Curriculum. This school has also improved the competence of the teachers about 2013 curriculum, and facilitates them with the sophisticated learning media.

Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes.

Selection hyper-heuristics (HHs) are randomised search methodologies which choose and execute heuristics during the optimisation process from a set of low-level heuristics. A machine learning mechanism is generally used to decide which low-level heuristic should be applied in each decision step. In this article, we analyse whether sophisticated learning mechanisms are always necessary for HHs to perform well. To this end we consider the most simple HHs from the literature and rigorously analyse their performance for the LeadingOnes benchmark function. Our analysis shows that the standard Simple Random, Permutation, Greedy, and Random Gradient HHs show no signs of learning. While the former HHs do not attempt to learn from the past performance of low-level heuristics, the idea behind the Random Gradient HH is to continue to exploit the currently selected heuristic as long as it is successful. Hence, it is embedded with a reinforcement learning mechanism with the shortest possible memory. However, the probability that a promising heuristic is successful in the next step is relatively low when perturbing a reasonable solution to a combinatorial optimisation problem. We generalise the "simple" Random Gradient HH so success can be measured over a fixed period of time , instead of a single iteration. For LeadingOnes we prove that the Generalised Random Gradient (GRG) HH can learn to adapt the neighbourhood size of Randomised Local Search to optimality during the run. As a result, we prove it has the best possible performance achievable with the low-level heuristics (Randomised Local Search with different neighbourhood sizes), up to lower-order terms. We also prove that the performance of the HH improves as the number of low-level local search heuristics to choose from increases. In particular, with access to low-level local search heuristics, it outperforms the best-possible algorithm using any subset of the heuristics. Finally, we show that the advantages of GRG over Randomised Local Search and Evolutionary Algorithms using standard bit mutation increase if the anytime performance is considered (i.e., the performance gap is larger if approximate solutions are sought rather than exact ones). Experimental analyses confirm these results for different problem sizes (up to ) and shed some light on the best choices for the parameter in various situations.

China’s Development of ETS as a GHG Mitigating Policy Tool: A Case of Policy Diffusion or Domestic Drivers?

AbstractChina launched its national emissions trading scheme (ETS) in late 2017. This article examines the key drivers behind China’s 2011 decision to opt for ETS as a GHG mitigating policy tool and what lay behind the choice of the system’s design features. Given the existence of the frontrunner EU ETS and that market mechanisms have spread across the world in recent years, we analyze the role played by policy diffusion in the decision to launch an ETS and in the subsequent design process, seen in relation to domestic drivers. The article investigates policy developments culminating in the 2011 carbon market announcement, and the reasons these design elements were chosen for the pilot schemes and the national market in the period 2011–2017. The article contributes to our understanding of policy diffusion at different stages of policy development in China, by revealing which diffusion mechanism is more prevalent at different stages. We find first that overall domestic conditions and drivers had the most consistent impact on policy decisions to establish a carbon market and on the selected sectors. However, a second key finding is that the role of policy diffusion varied over time, with such diffusion, in the form ofideational impact, playing the most important role early on, providing a powerful inducement for China to go for a carbon market. Third, sophisticated learningfrom international projects took place in the pilots, allowing China to adapt policies and design features to match local conditions.

Beyond Information Recall

Multiple-choice questions have an undeserved reputation for only being able to test student recall of basic facts. In fact, well-crafted mechanically gradable questions can measure very sophisticated cognitive skills, including those engaged at the highest level of Benjamin Bloom’s taxonomy of outcomes. In this article, I argue that multiple-choice questions should be a part of the diversified assessment portfolio for most philosophy courses. I present three arguments broadly related to fairness. First, multiple-choice questions allow one to consolidate subjective decision making in a way that makes it easier to manage. Second, multiple-choice questions contribute to the diversity of an evaluation portfolio by balancing out problems with writing-based assessments. Third, by increasing the diversity of evaluations, multiple-choice questions increase the inclusiveness of the course. In the course of this argument, I provide examples of multiple-choice questions that measure sophisticated learning and advice for how to write good multiple-choice questions.

Learners’ perspectives on Life Orientation sexuality education in South Africa

ABSTRACTThis article examines the views of male and female learners regarding how Life Orientation (LO) sexuality education is taught at their schools. Learners in the study were selected from five former ‘Black’ schools in the Eastern and Western Cape Provinces of South Africa. Focus groups were used to identify what learners could recall about their LO sexuality education classes. The strong trend in the data speaks to how LO sexuality education implies a gendered, heteronormative and moralistic approach to youth sexuality which silences and negates same sex relationships and girls’ accounts of sexuality. Although LO sexuality curricula are, as crafted on paper, often sophisticated learning programmes, participants point to a disjuncture between the official LO sexuality education curriculum and how LO sexuality education is taught in the studied schools. The paper concludes with some specific recommendations for teachers to promote a non-judgemental approach to sexuality education that challenges heteronormativity and other gendered injustices as part of the teaching of LO sexuality education.

The effectiveness of using augmented reality (AR) on assembling and exploring educational mobile robot in pedagogical virtual machine (PVM)

ABSTRACT Augmented reality (AR) has shown potential for aiding users in their assembly tasks. It provides a magic-lens view of the physical object. Thus, assembly tasks become less demanding. An assembly task using AR provides a pre-defined sequence of actions with a minimum amount of information to do the task. Thus, there is a need to exploit the advances in AR technology to provide assemblers with a more sophisticated learning experience. Using AR to turn the physical object into a smart object that communicates and interacts with a user can result in an assembler achieving a more knowledge and a greater awareness of the technology and provide insights and a deeper understanding of the software components inside the embedded computing instead of only learning the assembly hardware components. This study used a Pedagogical Virtual Machine (PVM) to evaluate the learning effectiveness with AR. We presented an experimental work based on assembling and exploring a modularized mobile robot task called Buzz-Boards. A total of 36 students participated in the study; 18 students participated in both experimental and control groups. Findings demonstrated that the PVM with AR approach was more effective in learning achievement, learning activity enjoyment, and usefulness than the paper-based approach.

Online Interactivity – A Shift towards E-textbook-based Medical Education

Abstract Textbooks have played the leading role in academic education for centuries and their form has evolved, adapting to the needs of students, teachers and technological possibilities. Advances in technology have caused educators to look for new sources of knowledge development, which students could use inside and outside the classroom. Today’s sophisticated learning tools range from virtual environments to interactive multimedia resources, which can be called e-textbooks. Different types of new educational materials that go beyond printed books are now used to support the academic curriculum, with the most interesting ones exemplified in this article to show their value in medical and life sciences education. Certain interesting attempts by Polish publishers at applying technology to support both self-paced and tutor-paced student learning in the medical profession and related areas are presented. The data was collected through a review of literature, discussions with e-learning specialists and medical students, as well as an analysis of examples of good practices. The article also discusses the results of a countrywide survey on the use of e-textbooks to enhance medical education. The research investigates the targeted academic staff’s attitudes towards the use of e-textbooks, the limitations of their implementation, and the character of e-resources currently used by Polish higher education medical institutions.

Deterministic response strategies in a trial-and-error learning task

Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in the presented trial-and-error learning task go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning.