Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies, such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community since different research works employ the adjective “smart” to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency and also to contribute to a common understanding of the SLE concept, this article presents a systematic literature review of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty-eight papers out of an initial list of 1341 papers were analyzed to identify the following: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this article presents a comprehensive characterization to describe SLEs through their affordances, the technologies used, and pedagogical approaches considered in the selected papers. As a result, specific core functions of SLEs are identified and explained. This work aims at ensuring a relevant knowledge base and reference toward the implementation of future SLEs.

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

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A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

The purpose of this study was to demonstrate a new design of low-cost and portable laboratory kit that is prospective for supporting teaching and learning on the automation process. The kit consists of the water tank filling system (sizes of 50 mL; as a model for describing reallistic tank in the plant) equipped with a programmable logic controller (PLC) integrated with SCADA system, human machine interface (HMI) monitor, reservoir, temperature, water level sensors, mixer, and heater. To be adaptable in any types of classroom, the kit was placed on the portable table (length x width x height of 100 x 50 x 150 cm). To approach the industrial tank system in industry, the tank was designed to be mixed and connected to other tank, and the temperature and water volumetric (water level) was controllable. To examine the impact of the designed kit on the improvement of teaching and learning process, the problem based learning (PBL) approach was also conducted in class. The economic analysis result showed that the present kit is inexpensive and portable, compared to other commercially available kits/devices. The PBL results showed that the kit is simple and to give better illustrations for students to comprehend the process control system in the realistic application in industry. Further developments of this kit is potentially implemented as an experimental tool for undergraduate students.

Low-cost and Portable Process Control Laboratory Kit

In order to solve the local path planning problem of mobile robot in the unknown environment, more complex control algorithms and solutions are needed for various special situations; however, they cannot be suitable for every environment. A local path planning algorithm of the mobile robot based on Long Short-Term Memory (LSTM) neural network is proposed to simplify the complexity of control, increase the ability of learning and generalization, and improve the efficiency of path planning. Firstly, the model structure of LSTM neural network is designed based on the task requirements of the local path planning of mobile robot. Then, the training data needed by the neural network are collected based on the fuzzy control algorithm and used to train the LSTM model. Finally, the trained model is tested according to the local path planning task. The experimental results show that compared with the fuzzy control algorithm, the designed method improves the calculation velocity, and can adapt to the environment with more obstacles compared with the BP neural network algorithm.

Local Path Planning of Mobile Robot Based on Long Short-Term Memory Neural Network

This paper proposes a geometric strategy for generating optimal navigation paths for robots in static and observable environments. As design principles for the algorithm, robustness, simplicity and low computational cost were established. The algorithm uses image segmentation into regions as architecture support. These regions are constructed from the vertices of the obstacles, which define two independent sets of regions. From the boundaries between regions we build a set of secure navigation points, points that are the basis for generating a bank of secure paths. Finally, a search algorithm selects, from this bank, the path that the robot will follow. The algorithm has been successfully evaluated by simulation on over 30 environments, finding a suitable path in 100% of the cases.

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Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación de Imágenes

A major part of the success of human-robots integration requires the development of robotic platforms capable of interacting in human environments. Human beings have an environment designed for their physical and morphological capacity, robots must adapt to these conditions. This paper presents a fuzzy-sliding hybrid grasp control for a five-finger robotic hand. As a design principle, the scheme takes into account the minimum force required on the object to prevent the object from slipping. The robotic hand uses force sensors on each finger to determine the grasp state. The control is designed with two control surfaces, one when there is slippage, the other when there is no slippage. For each surface, control rules are defined and unified by means of a fuzzy inference block. The proposed scheme is evaluated in the laboratory for different objects, which include spherical and cylindrical elements. In all cases, an excellent grasp was observed without producing deformations in the fragile objects.

Hybrid fuzzy-sliding grasp control for underactuated robotic hand

—The growing and marketing of coffee is an impor- tant source of economic resources for many countries, especially those with economies dependent on agricultural production, as is the case of Colombia. Although the country has done a lot of research to develop the sector, the truth is that most of its cultivation is carried out by small coffee families without a high degree of technology, and without major resources to access it. The quality of the coffee bean is highly sensitive to diverse diseases related to environmental conditions, fungi, bacteria, and insects, which directly and strongly affect the economic income of the entire production chain. In many cases the diseases are transmitted rapidly, causing great economic losses. A quick and reliable diagnosis would have an immediate effect on reducing losses. In this sense, this research advances the development of an embedded system based on machine learning capable of performing on-site diagnoses by untrained personnel but taking advantage of the know-how of expert coffee growers. Such a system seeks to instrument the visual characteristics of the most common plant diseases on low-cost, robust, and highly reliable hardware. We identified a deep network architecture with high performance in disease categorization and adjusted the hyperparameters of the model to maximize its characterization capacity without incurring overfitting problems. The prototype was evaluated in the laboratory on real plants for recognized disease cases, tests that matched the performance of the model validation dataset.

A Machine Learning Model for the Diagnosis of Coffee Diseases

This paper shows the use of an embedded robotic platform of low cost and high performance, hand in hand with problem-based learning strategies, to professional training in Electrical Engineering at the District University Francisco Jose de Caldas (Colombia). These technical training and research tools involves several innovations, among which stand out the robot itself, which is inexpensive, robust and with high performance (suitable for both training and research), the study of real problems and the support with software tools that complement a smart learning environment. The robot has a mechanical differential platform that is easy to build and modify, a processing hardware supported in a 900 MHz quad-core ARM Cortex-A7 CPU able to run a graphical OS, and ROS as communication and control software. As advantages of its implementation has documented a better appropriation of theoretical concepts, increased student enthusiasm, improved ease of communication and teamwork, and greater interest in participation in research activities.

Using Embedded Robotic Platform and Problem-Based Learning for Engineering Education

This paper shows the results of the automatic design of a generic heat sink, through a morphogenesis algorithm done in Matlab. This algorithm is based on two-dimensional fractal images, generated by Lindenmayer systems, which create the heat sink shape. Those shapes iteratively evolve through a genetic algorithm in order to maximize their heat dissipation capability, estimating it through measurements of their surface and volume. Evaluation process was supported by image processing algorithms. Finally finite elements simulations are carried out in order to determine the real heat dissipation capability of each design and thus obtaining some valid heat sink shapes.

Holman Montiel

Papers

Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación de Imágenes

Hybrid fuzzy-sliding grasp control for underactuated robotic hand

A Machine Learning Model for the Diagnosis of Coffee Diseases

Using Embedded Robotic Platform and Problem-Based Learning for Engineering Education

Automatic design of heat sink using genetic algorithms, Lindenmayer systems and digital image processing