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The history of Organizational Development (OD) reveals a much older tradition of organizational science than the conventional wisdom would suggest. By the 1960s and 1970s OD became self‐confident and dynamic. This period was not only highly experimental but established the principles of OD for much of the twentieth century. By the end of the twentieth century new images of OD had occurred and much of the earlier thinking had been transformed. This review illustrates some examples under a series of themes that have had a major impact on the discipline of OD and on the wider thinking of organizational theorists and researchers.

The purpose of this study is to define characteristics and processes that enhance informal learning in a public sector workplace.

Based on interviews and questionnaires, the authors solicited examples of informal learning practices that 40 supervisors experienced during their careers. The examples were content analyzed to define seven broad themes underlying informal learning.

The findings illustrate seven broad themes describing learning activities and processes. The first three themes describe the types of informal learning activities that supervisors found valuable: relationships; learning opportunities enlarging or redesigning their jobs; and enrichment opportunities that provided higher levels of managerial learning. Four themes describe processes for facilitating informal learning: planning processes; active learning and modelling; relationship dynamics; and tying learning to applications.

The value of this study is in presenting a possible framework defining informal learning that describes both activities (the what) and the underlying processes (the how) by which they are delivered. Beyond this, it suggests that there is a close connection between the activities and the processes underlying them.

The purpose of this study is to investigate students’ decisions in example-based instruction within a novel self-regulated learning context. The novelty was the use of automated generators of worked examples and problem-solving exercises instead of a few handcrafted ones. According to the cognitive load theory, when students are in control of their learning, they demonstrate different preferences in selecting worked examples or problem solving exercises for maximizing their learning. An unlimited supply of examples and exercises, however, offers unprecedented degree of flexibility that should alter the decisions of students in scheduling the instruction.

ASolver, an online learning environment augmented with such generators for studying computer algorithms in an operating systems course, was developed as the experimental platform. Students’ decisions related to choosing worked examples or problem-solving exercises were logged and analyzed.

Results show that students had a tendency to attempt many exercises and examples, especially when performance measurement events were impending. Strong students had greater appetite for both exercises and examples than weak students, and they were found to be more adventurous and less bothered by scaffolding. On the other hand, weak students were found to be more timid or unmotivated. They need support in the form of procedural scaffolding to guide the learning.

This study was one of the first to introduce automated example generators for studying an operating systems course and investigate students’ behaviors in such learning environments.

Presents an inductive machine learning algorithm called CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. Analyses the complexity of the algorithm and compares the results with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy which are not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. The study is published in two parts: I – the CLILP2 algorithm; II – experimental results and conclusions.

This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process.

These multistrategy machine learning student modeling techniques include inductive reasoning (similarity‐based learning), deductive reasoning (explanation‐based learning), and analogical reasoning (case‐based reasoning).

According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring.

This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.

ALFS is an inductive learning algorithm that employs feature selection to learn concepts from examples. Features which best represent and differentiate a subset from other subsets in learning data are detected and used to produce rules. These rules form a knowledge base for an expert system. The performance of ALFS is illustrated using data sets from the domains of primary tumour and game playing.

A learning algorithm called CLILP2 (Cover Learning Using Integer Linear Programming) is applied to medical data to generate rules to recognize patients with coronary artery disease. The algorithm partitions a data set into subsets using features which best describe and distinguish a particular subset from all other subsets. These features are used to form the rules which can be used as the knowledge base of a diagnostic expert system. Results from the application of the algorithm to coronary artery stenosis data are compared with the results obtained from the existing expert system.

Reviews a range of theories, concepts and learning approaches that are relevant to the development of professionals. Goes on to take a look at how professionals actually learn, once they are in practice. The latter is based on empirical research conducted across 20 professions. Reports on the range of experiences and events that practitioners had found particularly formative in helping them become fully competent professionals; this point often not having been reached until long after their formal professional training had ended. An attempt is made to relate the formative experiences reported to particular theoretical approaches to learning. The experiences are classified into a number of general kinds of “learning mechanism” and these are placed within a “taxonomy of informal professional learning methods”. The results of the research should be of use both to professional developers and to individual professionals. They should assist developers in their planning of placements or post‐formal training. They should help individual professionals to maximise their professional learning, by seeking out particular kinds of experience and making the most of those that come their way.

The purpose of this paper is to present a theoretical framework, which describes how police training programs can be developed in order to improve learning retention and the transfer of skills to the work environment.

A brief review is provided that describes training strategies stemming from Cognitive Load Theory (CLT), a well‐established theory of instructional design. This is followed by concrete examples of how to incorporate these strategies into police training programs.

The research reviewed in this paper consistently demonstrates that CLT‐informed training improves learning when compared to conventional training approaches and enhances the transferability of skills.

Rarely have well‐validated theories of instructional design, such as CLT, been applied specifically to police training. Thus, this paper is valuable to instructional designers because it provides an evidence‐based approach to training development in the policing domain.

This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?”

This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project.

The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system.

The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.