Controlling the slides: Does clicking help adults learn?

When utilizing screen media as an educational platform, maintaining control over one's experience may lead to more successful learning outcomes. In the current work, adults learned four new action sequences, each via a different slideshow type. The computer advanced slides automatically, but each version had a different pausing mechanism: (1) free pause (viewers could click the mouse at any point to pause the show), (2) subgoal pause (show paused after subgoals, viewer clicked to continue), (3) timed pause (show paused every 20 slides, viewer clicked to continue), and (4) no pause (no viewer interaction). Participants completed a written memory test, live performance test, cognitive load measures, and satisfaction measures. Results indicated that memory recall was significantly lower in the no pause version when compared to the versions with pause capability. Also, over half of participants reported that the no pause version was their least favorite format to learn from. Conversely, over half of participants selected the free pause as their favorite slideshow format, and participants reported that they felt most in control of the free pause version. These reports occurred in spite of only one-quarter of all participants actually using the click-to-pause feature in the free pause slideshow. Perhaps the mindset of being in control, rather than the pausing itself, increased likeability of the program. This research has implications for program design and education, pointing to flexible pacing features being helpful in enhancing users' enjoyment of the program and ability to extract novel information.     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Learning Organizations in Construction

The transformation of organizations from production-oriented entities to proactive learning entities that continuously leverage the knowledge of the workforce is a primary objective of management researchers. This focus has significant relevance to the construction industry where production-related research has predominantly overshadowed organizational development research. As one effort to change this emphasis, the writers present a research effort designed to study current organization learning techniques and technologies fielded by organizations both inside and outside of the construction industry. Through a series of exploratory case studies, the writers developed a maturity model together with the Construction Industry Institute that provides construction organizations with a framework for developing a learning organization culture. The maturity model focuses on learning organization characteristics of leadership, processes and infrastructure, communication/collaboration, education, and culture at the organization, community, and individual levels. This paper introduces the results of that effort including a presentation of the learning organization maturity model, framework application, and the overall characteristics of a learning organization.     

Why is science difficult to learn? Things are seldom what they seem

Abstract The difficulties of learning science are related to the nature of science itself and to the methods by which science is customarily taught without regard to what is known about children's learning. An information processing model is proposed to guide thinking and research in this area.     

Pruning Algorithms for Rule Learning

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.     

Using process-oriented, sequencing educational technologies: Some important pedagogical issues

In recent times, sequencing technologies are becoming increasingly used, both by the university and the industry sectors. In essence, these complex systems support sequencing of, and navigation through units of content. They come in two different types: SCORM-based and workflow-based systems. Current research efforts related to sequencing technologies concentrate mostly on technical issues, while the associated pedagogical issues remain unexplored.The main objective of this paper is to describe the most important pedagogical issues that need to be taken into account when implementing any type of sequencing educational technologies (either SCORM- or workflow-based). These issues were identified during an action-learning project related to the practical implementation of a workflow-based educational system. During the reflection phase of this project, these issues were then genarised, so they could be applied to any type of sequencing technology and in any application domain (teaching discipline). The paper describes how sequencing technology can be used to enable a more flexible learning experience (especially in terms of time and flexible learning pathways) and then proceeds with the detailed analysis of the associated pedagogical issues.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.