A methodology for characterizing computer-based learning environments

Text‐to‐speech technology can act as an important support tool in computer‐based learning environments (CBLEs) as it provides auditory input, next to on‐screen text. Particularly for students who use a language at home other than the language of instruction (LOI) applied at school, text‐to‐speech can be useful. The CBLE E‐Validiv offers content in the LOI and one of six other languages. All content can be read aloud via text‐to‐speech. For students having a home language other than the LOI, the other language is set to their home language; students who use the LOI at home mostly have English or French available. This study aimed to determine fifth‐grade students' use of bilingual text‐to‐speech and examine student characteristics related to this use (n = 360). Multilevel hierarchical regression analyses show that particularly students having their home language available apply text‐to‐speech in their home language. However, their main focus remains on text‐to‐speech in the LOI. Students with a low self‐assessed proficiency in their home language and those who often watch television and read books in the LOI use text‐to‐speech more in the LOI. Considerations for practice, the design of CBLEs with text‐to‐speech and future directions for research are discussed.Lay DescriptionWhat is currently known about the subject? Text‐to‐speech (TTS) technology converts digital text to spoken language. Combining visual and auditory information extends working memory's capacity. TTS has potential to offer bilingual auditory support to language minority students. Learner characteristics may play a considerable role in the use of TTS. What does this paper add? Language minority students use TTS in the language of instruction and their home language. TTS in the home language is mainly applied as a support to strengthen understanding. Not being confident about the proficiency in the home language indicates a stronger focus on TTS in the language of instruction. Reading books and watching television in the language of instruction points to a stronger use of TTS in that language. What does this mean for practitioners? The computer‐based learning environment can support students in meaning construction. TTS can assist language minority students both in the language of instruction and the home language. Students who are not confident in their home language need special attention.

This chapter outlines key findings of cognitive and developmental psychology which could be used as a theoretical framework to guide the design and research of e-learning applications. The chapter consists of two main sections. The first section presents the basic cognitive mechanisms and their development, while the second part discusses how our knowledge of the cognitive system can guide the design of computer-based learning environments. It is proposed that the human mind is organized in three levels: two of them are general-purpose mechanisms and processes and one consists of domainspecific structures of knowledge representation and problem solving processes. These three levels are associated with an effective learning framework. The suggested framework describes the basic points that any designer of e-learning environments must consider. The second section of the chapter discusses e-learning, connecting it to knowledge about the human cognitive system. In this section, we first present the conceptual bounds of the e-learning construct and discuss how cognitive theories of learning should guide the design of technologically-enhanced learning environments. It is proposed that a synergy between the study of human cognition and the design of e-learning applications is required for effectively understanding both fields.

Designers of computer-based practice systems give primary attention to aspects of cognitive learning. However, observations of students practicing arithmetic, with two CAI systems widely used in Israel, revealed factors in the computer learning environment that substantially affected students' social behavior, particularly with respect to competition and cooperation in the course of learning. A questionnaire was designed on the basis of the observations to examine the effects of these factors in CAI work. It was administered to 457 fourth graders in 16 classes (two CAI systems x two SES categories x two schools x two classes). While neither CAI system was intended to foster competition, the observations and questionnaire data suggested that one encourages social comparison and competition whereas the other discourages these features and encourages cooperation and peer help. Boys appear to be significantly more competitive than girls. One system, compared with the other, significantly increases competitive behavior of high achievers (but not of low achievers); results in teachers' greater encouragement of social comparison and competition among students; and, as used in the schools we studied, causes disadvantaged students rather than advantaged students to develop negative feelings because of low CAI performance. The other system discourages competitive behavior and encourages cooperation. All these findings bear important implications for the instructional design of computer-based learning environments.

<div class="abstract_container"> <strong>Abstract:</strong> This paper examines two sides of a coin that relate to learning from dialogue. The first side of our coin relates to the role of dialogue in learning; the second side is related to the part that observations of learning dialogues can play in the design of computer-based learning environments. In order to define the scope of the paper, various complementary research question are examined. For example, one question is how and why does one learn from dialogue? A second question is how, or to what extent, can theories and studies of dialogue and interaction be exploited in a concrete way by designers of interactive media for education? Following a review of related literature I present my main argument, namely that the evolutionary approach provides a model by which research in learning, teaching and theories of interaction can jointly feed into the design process of learning technology. This is followed by an exposition of the evolutionary approach. There are three aspects to what I am terming an evolutionary approach to learning technology theory: (i) theories/models of teaching, learning and interaction, (ii) empirical observations of learning, and (iii) interactive learning environment design and implementation. The purpose of this evolutionary approach is the mapping out of not a specific theory, but how people are working towards the creation of theories. The evolutionary approach involves a constant process which slowly takes the educational technology field forward in iterative steps. In order to concretise the evolutionary approach, I examine the work of selected researchers, in the field of dialogue in learning, in the context of the identified three points of evolution. I conclude by suggesting that the evolutionary model can help designers of, and researchers into, learning technology in various important ways. </div> <p class="editors_container"> <strong>Editors:</strong> <A href="http://www.ucl.ac.uk/epd/profiles/mo.html" target=xref>Martin Oliver</A> (UC London, UK) <p class="reviewers_container"> <strong>Reviewers:</strong> Sarah Fletcher (U. Bath, UK), Roger Hartley (U. Leeds, UK), Colleen McKenna (UC London, UK)

Currently, there exists a lack of consensus regarding the types and purposes of scaffolding features within the educational design community. In this work, we seek to establish a common framework of scaffolding types as a grounded reference for researchers and designers of computer-based learning environments. We looked to three successful computer-based learning environments, PhET Interactive Simulations, ETL E-slate Microworlds, and Young Researcher for examples of designed-in features that serve as scaffolds – finding five general scaffolding types. We present descriptions and examples of each scaffolding type, which includes scaffolds for scope of domain knowledge, inquiry pathway, sequencing, feedback, and cueing.

Abstract: The aim of this study is to discover which kind of scaffolding can effectively promote learning. The past studies have shown mixed results in this regard. The process in which a domain expert gives and withdraws support in order to make a novice learner complete the task is known as scaffolding. A total of four distinct scaffold combinations and four groups were made. This experimental study was repeated twice to cross verify the outcomes using computer based learning environment (CBLE). The CBLE was designed with intelligent web program in PHP and jQuery to evaluate the solutions submitted by the learners instantly. The CBLE acted as an intelligent feedback system. In the first study, it was found that there was a significant effect of different scaffolding treatments on the learning outcomes, F (3,76) = 5.762, p=.001. The result analysis involves multiple comparisons based on Tukey HSD test and indicated that the mean score for the indirect support and adaptive fading (M=4.45, SD=1.191) was considerably different than the others. Likewise, second study also found that there was a significant effect of different scaffold treatments on the learning outcome, F (3,76) = 4.258, p=.008. The Tukey HSD test applied during the second study indicated that the mean score for the indirect support and adaptive fading (M=4.55, SD=1.19) was again significantly different than the others. The present study additionally measured the flow state of all the four groups using Kruskal-Wallis H test and found that indirect support and adaptive fading group was significantly different than direct support and adapting fading group as well as direct support and gradual fading group in both the studies. Keywords:Computer Based Learning Environment (CBLE),

Foreword ( Wilbert J. McKeachie). 1. Introduction: Self-Regulation of Learning in Postsecondary Education (Hefer Bembenutty) The chapter introduces this volume on self-regulation of learning and highlights the current trends on self-regulation presented by each of the authors. 2. Purpose of Engagement in Academic Self-Regulation (Einat Lichtinger, Avi Kaplan) This chapter argues that self-regulated learning is not a unitary construct, with students different purposes of engagement in the task meaningfully distinguishing between different types of self-regulation. 3. Self-Regulation and Achievement Goals in the College Classroom (Akane Zusho, Kelcey Edwards) This chapter links self-regulation of learning with achievement goal theory and offers practical tips to educators who struggle with disinterested learners. 4. Understanding and Facilitating Self-Regulated Help Seeking (Stuart A. Karabenick, Myron H. Dembo) The authors describe interventions to develop the competencies and resources that facilitate student help seeking as an effective selfregulated learning strategy. 5. Self-Regulation and Learning Strategies (Claire Ellen Weinstein, Taylor W. Acee, JaeHak Jung) This chapter reviews research on learning strategies, describes a model of strategic and self-regulated learning, and reports on instructional methods, interventions, and assessments that instructors can use to help students develop lifelong learning strategies they need to succeed in college. 6. Academic Delay of Gratifi cation and Academic Achievement (Hefer Bembenutty) This chapter provides a review of research on delay of gratifi cation and suggests ways in which educators can instill in students to delay gratifi cation. 7. Resistance and Disidentifi cation in Refl ective Practice with Preservice Teaching Interns (Michael Middleton, Eleanor Abrams, Jayson Seaman) This chapter examines case studies to identify contextual factors in teacher education programs that promote or inhibit teaching interns' self-refl ective practices. 8. Professional Development Needs and Practices Among Educators and School Psychologists (Timothy J. Cleary) This chapter summarizes research illustrating the importance and need for motivation and self-regulation professional development training for teachers and school psychologists across assessment, intervention, and instructional activities. 9. Transitioning from College Classroom to Teaching Career: Self-Regulation in Prospective Teachers (Judi Randi, Lyn Corno, Elisabeth Johnson) This chapter explores how preservice teachers prepare for the transition from college classroom to career through assignments and features of the learning environment designed to approximate the demands of work settings and job-related tasks. 10. The Role of Web 2.0 Technologies in Self-Regulated Learning (Anastasia Kitsantas, Nada Dabbagh) This chapter demonstrates how Web 2.0 technologies can be used to facilitate self-regulated learning in postsecondary education. The authors show how instructors can integrate social software into course design to promote students self-regulation of learning. 11. Self-Regulation of Learning with Computer-Based Learning Environments (Jeffrey A. Greene, Daniel C. Moos, Roger Azevedo) This chapter outlines how self-regulated learning skills can facilitate learning with computer-based learning environments and how educators can diagnose and build on students' self-regulated learning ability. 12. New Directions for Self-Regulation of Learning in Postsecondary Education (Hefer Bembenutty) This chapter puts into context the major contributions of this volume on self-regulation of learning and provides new directions for its promotion in postsecondary education. INDEX.

This contribution considers computer-based learning environments from a socio-cultural perspective. It relates the design of learning environments with the concept of internalization, i.e the genetic link between social speech, private speech and inner speech. We translate these levels in terms of user-system interactions and analyze the transition between levels.

Complex problem solving and learning in a complex technology environment are closely connected with non-transparent situations. They contain several goals and variables and are, therefore, difficult to perceive and solve. Complex problems and complex learning situations are typically semantically rich in character and an understanding of their structure will require complex conceptual and relational domain knowledge. Expert’s performance in demanding design tasks and in complex problem solving situations begins with a deep structuring process because little knowledge is available of the goal and of the starting point. All this has some serious implications for the organization of learning situations and environments. In this chapter I will discuss some consequences which the above research results and ideas can have for the development and automating instructional design of computer-based learning situations and environments.

Instructional designers use learning analytics information to evaluate designs of learning environments, learning materials, and tasks, and adjust difficulty levels, as well as measure the impact of interventions and feedback. Integrating real-time educational data and analysis into the design of learning environments, referred to as learning analytics design (LAD), seems to be a promising approach. Valid pedagogical recommendations may be suggested on the fly as learning analytics methodologies and visualizations evolve and as reliable tools become available and ready for classroom practice. This chapter aims to offer an overview on design and analytics of learning environments before reviewing opportunities of learning analytics design for optimizing learning environments in near real time. Learning analytics (LA) use static and dynamic information about learners and learning environments—assessing, eliciting, and analyzing them—for real-time modeling, prediction, and optimization of learning processes, learning environments, and educational decision-making.

The Exploitation of Traces Serving Tutors for the Reconstruction of Groups within a CBLE

Feedback is an essential aspect of computer-based learning environments (CBLE). However, the effect of specific types of feedback on the mathematics performance and self-rating ability of students has not yet been definitively established. To resolve this knowledge gap, we plan to conduct a (quasi-) experimental study with 8th- and 9th-grade students in high schools (Gymnasium) in North Rhine-Westphalia, Germany. The study is going to use a MediaWiki-based CBLE named “Discover Quadratic Functions”. This chapter gives an overview of existing research on CBLE design principles and feedback types. Moreover, the theoretical decisions underlying the design of the CBLE on quadratic functions are presented and explained. Finally, we focus on the results of a qualitative preliminary study, as well as the design of the upcoming main study.

This chapter discusses how cultural variables can be taken into account when designing computer-based learning environments (CLEs). Its purpose is to identify concrete recommendations to guide instructional engineering of computer-based learning for diverse cultures through a review of the literature on the subject. First, this chapter describes the background in which such recommendations have emerged, and identifies some of the issues underlying instructional design for diverse cultures. Then it introduces models and guidelines on how cultural variables can be taken into account when designing CLEs. Specific recommendations are organized using a method of instructional engineering for CLEs called MISA (Paquette, 2003) as a frame of reference. This is followed by a discussion on future trends and future research directions.

Benefits of inserting support devices in electronic learning environments

BackgroundIt has been assumed that prompting students to plan, monitor and evaluate their learning process could stimulate strategy use and thereby improve learning outcomes.ObjectivesThis study aimed to examine the effects of metacognitive prompts on students' self‐regulated learning (SRL) and learning outcomes in the context of computer‐based learning environments (CBLEs).MethodsTo achieve this, the current study took a meta‐analytic approach to critically evaluate evidence for the effectiveness of metacognitive prompts and identify potential moderators of the effects.Results and conclusionsWith random‐effects models, the results showed that metacognitive prompts significantly enhanced SRL activities (g = 0.50, 95% confidence interval [0.37, 0.63]) and learning outcomes (g = 0.40, 95% confidence interval [0.31, 0.49]) relative to the control conditions. Furthermore, moderator analyses revealed that the effects varied as a function of three prompts features: feedback, specificity and adaptability.ImplicationsDeveloping task‐specific, individual‐adaptive prompts and feedback should be a design principle in CBLEs, such that the prompt effect could be retained, sustainably enhanced and transferred to novel situations.