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The use of Active Learning (AL) techniques can significantly improve the teaching–learning process, as the content is explored in a more interactive, participative, and relaxed way. Although expositive classes are still broadly used in Brazil, in this chapter we present some AL techniques, as well as experiences of their application, used in Brazilian K-12, undergraduate, and graduate Information Systems courses. As a result, we have noticed learning has been more effective, and students have been motivated by the use of these AL techniques. Although used in the context of Information System courses, the techniques could be adapted to other scenarios.

The purpose of this chapter is to document how a new learning technic may create transformative learning in leadership in an organisational practice.

The learning methods developed in the learning in practice (LIP) project include aesthetic performances combined with reflections. The intention has been to explore how leadership may be transformed, when leaders work as a collective of leaders. The learning methods developed and tested in the LIP project are art-informed learning methods, concepts of liminality and reflection processes carried out in the leaders’ organisational practice.

One of the most important findings in the LIP project in relation to transformative learning is a new learning technique based on guided processes rooted in aesthetic performance combined with reflections and separation of roles as performer and audience. Reflection processes related to aesthetic performance serve as argument for the impact of ‘the audience wheel’.

Leaders who perform and reflect in a collective of leaders can better deal with complex organisational problems and enhance growing of welfare-in-the-making from an inside and out perspective. Moreover, the separation between classroom teaching and practical intervention will diminish when leaders learn aesthetic performance and reflections as a practical technique.

Drawing on Dewey’s accounts of learning the Alexander Technique (AT), this chapter explores why he found the process so powerful. As AT teachers, we explain how the technique enables practitioners to become aware of fixed, unconscious habits and to bring them under conscious control. With a new student, work begins with physical habits. However, because physical, cognitive, emotional and social functionings are interdependent, AT lessons typically enable flexibility in each of these spheres. Dewey’s writings show his strong theoretical commitment to the idea of learning as practical and experiential. His AT lessons were truly revelatory in providing him with both direct, embodied experience of the power of habit to drive human behaviour and a practical means of becoming aware of, and resisting, his own habits of thought and action.

Perceptions are shaped by habit in such a way that the senses can be unreliable in working out how to respond in a given situation. Dewey’s practice of the AT revealed to him the dissonance between his habitual self in activity and his conscious view of himself. Dewey was challenged by his AT lessons, which required an open, enquiring attitude and sense of humility. In the AT, Dewey found a means of pursuing an active, critical, self-directed process of discovery and adaptation akin to childhood learning. AT begins with the self, our ‘tool of tools’. Through fundamentally modifying the self, the AT supports the openness and flexible response to the physical and social world that characterize productive experiential learning.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.

Social and technological changes of the 21st century influence how and what students learn while in college. New research about student learning suggests a critical need for higher education to reform teaching and learning methods. Experiential and inquiry-based learning (IBL) are essential to engaging students and achieving the type of learning demanded by today’s global workforce. These skills include critical analysis, systems thinking, problem-solving, and spanning cultural and disciplinary boundaries. For decades, international educators purported that education abroad provided these skills for participants, yet recent research suggests that the same factors inhibiting deep learning on campus can also affect global, experiential environments. No longer can faculty members assume that students will learn from experience alone; they must intentionally construct activities accounting for the specific characteristics and needs of learners. This chapter outlines trends influencing student learning, making the case that traditional, content-based, directed instruction is poorly suited for student learning in the 21st century. The authors suggest that applying experiential and inquiry-based practices is essential to constructing effective education abroad program. Case studies, strategies, tools, and resources are provided to assist faculty with developing competencies to teach through an experiential and inquiry-based pedagogical framework.

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

Degree programs in Information Systems typically include a course on Systems Analysis and Design which is challenging to teach for instructors and challenging to grasp for learners. The central issue is that conditional knowledge, or when to use concepts and techniques in this domain, is seldom, if ever, taught. This chapter explains the ongoing evolution of a course on Systems Analysis and Design with the goal of developing conditional knowledge in students. It follows the process of changing course objectives, developing expert models of problem solving, experiencing challenges in delivering this kind of content, and reflecting on new insights that will improve course design in the future.

This chapter presents innovative approaches to active learning that were introduced into the teaching of preservice teachers at the Faculty of Education of University of West Bohemia, Pilsen, in the Czech Republic. Over the last three years, the Technology-Enhanced Learning course has seen substantial innovations in both the content and use of teaching strategies designed to prepare the students for their professional lives. The whole update of the course was implemented using the results of action research – all individual changes were rigorously tracked and analyzed. The state of the art in the active learning domain in education of preservice teachers is presented in this chapter.

There is a description of the procedure to update the course, based on the reflections of teachers and feedback from students, gathered during action research. Detailed evaluations of particular methods of active learning that have been proven in teaching are provided.

Besides practical activities with tablets and smartphones, during which students familiarize themselves with various types of applications and reflect on their use in teaching, the course was extended by the use of practical aids for the efficient inclusion of mobile technologies for teaching – the Czech version of Allan Carrington’s Padagogy Wheel. This aid is derived from the revised Bloom’s taxonomy and SAMR model and helps the systematic reflection of preservice teachers when preparing for technology-enhanced teaching.

A significant part of the teaching consists of cooperative projects between preservice teachers and pupils of elementary schools – for example, the preservice teachers help elementary school pupils discover possibilities of virtual reality during Google Cardboard activities, or preservice teachers in teams with elementary school pupils create digital stories together on the topic of Internet safety.

The innovative approach to active teaching in the Technology-Enhanced Learning course is apparent even during the exam. In the course of the exam, students process, present, and defend a lesson plan for the implementation of an activity using digital technologies.

Throughout the learning, as well as at the end, preservice teachers are encouraged to reflect on the teaching in the Technology-Enhanced Learning subject.