Search

We investigated the effects of just-in-time guidance at various stages of inquiry learning by novice learners. Thirteen participants, randomly assigned to an intervention (n = 8) or control (n = 5) group, were observed as they learned about DC electric circuits using a web-based simulation. Just-in-time instructional prompts to observe, predict, explain, systematically test, collect evidence, and generate rules were strongly associated with diagnosing and correcting misconceptions, and constructing correct scientific concepts. Students’ repeated use of predictions, systematic testing, and evidence-coordinated reasoning often led to formulating new principles, generalizing from observed patterns, verifying comprehension, and experiencing “Aha!” moments. Just-in-time prompts helped learners manage embedded cognitive challenges in inquiry tasks, achieve a comprehensive understanding of the model represented in the simulation, and show significantly higher knowledge gain. Just-in-time prompts also promoted rejection of incorrect models of inquiry and construction of robust scientific mental models. The results suggest ways of customizing guidance to promote scientific learning within simulation environments.

Need for cognition is conceptualized as an individual’s intrinsic motivation to engage in and enjoy effortful cognitive activities. Over the past three decades, there has been increasing interest in how need for cognition impacts and correlates with learning performance. This meta-analysis summarized 136 independent effect sizes (N = 53,258) for the association between need for cognition and academic achievement and investigated the moderating effects of variables related to research context, methodology, and instrumentation. The overall effect size weighted by inverse variance and using a random effects model was found to be small, r = .20, with a 95% confidence interval ranging from .18 to .22. The association between need for cognition and learning performance was moderated by grade level, geographic region, exposure to intervention, and outcome measurement tool. The implications of these findings for practice and future research are discussed.

Learning management systems (LMS) offer quiz tools that help students prepare for examinations. The purpose of this study is to investigate quiz tracking variables typically reported by LMS in relation to student achievement, motivation and learning strategies. The data from 143 undergraduate students comprised quiz tracking variables (number of attempts, completion time, and score), exam scores and responses to the Need for Cognition Scale (NfC), and selected components from the Motivated Strategies for Learning Questionnaire and the Achievement Goal Questionnaire. We hypothesized students retrieving information from memory while taking a quiz would complete the quiz in less time than students who searched for answers in the textbook, and consequently, quiz completion time would correlate with exam performance and key motivational and self-regulatory factors. We found quiz completion time correlated positively with performance-avoidance goal orientation. It correlated negatively with exam performance, NfC, self-efficacy, and effort regulation. The results indicated completion time of low stakes quizzes is associated with achievement-related motivations and reliably predicts achievement on summative exams. We attribute these links to the use of retrieval practice by students who successfully regulate their effort and learning strategies.

Scholarly understanding is limited with regard to what influences students' choice to take a particular course fully online or in-person. We surveyed 650 undergraduates at a public Canadian university who were enrolled in courses that were offered in both modalities during the same semester, for roughly the same tuition cost. The courses spanned a wide range of disciplines, from archaeology to computing science. Twenty-five variables were gauged, covering areas including students' personal circumstances, their competence in the language of instruction, previous experience with online courses, grade expectations, and psychological variables including their regulation of their time and study environment, work avoidance and social goal orientation. Two logistic regression models (of modality of enrolment and modality of preference) both had good fit to the data, each correctly classifying roughly 75% of cases using different variables. Implications for instructional design and enrolment management are discussed., Highlights • 650 undergraduates in several disciplines were surveyed. • All respondents were enrolled in courses offered both online and face-to-face. • Survey covered student circumstances, expectations and psychological variables. • Logistic regressions modeled modality of registration and modality of preference. • Psychological variables influenced preference more strongly than registration.

This study investigated how studying a refutational map, a type of argument map, affected conceptual change. Refutational maps visually display both correct and alternative conceptions. Participant...

Self-explanation is a process by which learners generate inferences about causal connections or conceptual relationships. A meta-analysis was conducted on research that investigated learning outcomes for participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = .55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts are a potentially powerful intervention across a range of instructional conditions. Due to the limitations of relying on instructor-scripted prompts, we recommend that future research explore computer-generation of self-explanation prompts.

A bottleneck in gathering big data about learning is instrumentation designed to record data about processes students use to learn and information on which those processes operate. The software system nStudy fills this gap. nStudy is an extension to the Chrome web browser plus a server side database for logged trace data plus peripheral modules that analyze trace data and assemble web pages as learning analytics. Students can use nStudy anywhere they connect to the internet. Every event related to creating, modifying, reviewing, linking and organizing information artifacts is logged in fine grain with a time stamp. These data fully trace information students operate on and how they operate on it. Ambient big data about studying gathered au naturel can be tailored by configuring several of nStudy’s features. Thus the system can be used to gather data across a wide range lab studies and field trials designed to test a range of models and theories.

A concept map is a node-link diagram in which each node represents a concept and each link identifies the relationship between the two concepts it connects. We investigated how using concept maps influences learning by synthesizing the results of 142 independent effect sizes (n = 11,814). A random-effects model meta-analysis revealed that learning with concept and knowledge maps produced a moderate, statistically significant effect (g =.58, p < .001). A moderator analysis revealed that creating concept maps (g =.72, p < .001) was associated with greater benefit relative to respective comparison conditions than studying concept maps (g = .43, p

Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gašević, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning episodes. They recommended building on Winne’s (1982) characterization of traces — ambient data gathered as learners study that more clearly represent which operations learners apply to which information — and his COPES model of a learning event — conditions, operations, products, evaluations, standards (Winne, 1997). We designed and describe an open source, open access, scalable software system called nStudy that responds to their challenge. nStudy gathers data that trace cognition, metacognition, and motivation as processes that are operationally captured as learners operate on information using nStudy’s tools. nStudy can be configured to support learners’ evolving self-regulated learning, a process akin to personally focused, self-directed learning science.

The term cognitive tool has been used in many areas of academic specialisation, where it has taken on multiple connotations. In this historical and systematic review, we investigate the conceptualisation of cognitive tools in the learning sciences and educational technology. First, the theory of cognitive tools vis-à-vis learning and development is traced from Vygotsky and Soviet psychology through to its use in current educational technology and learning design. Second, we present a systematic review of cognitive tools in peer-reviewed research literature. We found the term cognitive tool was often used vaguely or with extreme generality. When used more specifically, it referred to communication methods such as visualisations, metaphors, symbols, and hypermedia; or interactive interfaces and environments such as templates, databases, simulations, games, and collaborative media. We offer a definition of software-based cognitive tools founded on the attributes of representation, interactivity, and distributed cognition, which commonly feature in the work of influential theorists; and we explain implications of the definition for designing, evaluating, and researching learning technologies.

In the field of learning analytics, dashboards are visual displays that help instructors and students monitor performance, track goals and modify learning-related activities and plans. Student-facing dashboards provide visualizations of the data students need to take responsibility for their own learning, while instructor-facing dashboards help instructors guide and orchestrate student learning. After summarizing the spectrum of learning analytics research on dashboards, we critically review dashboards designed to support collaborative learning and examine research on student-facing and instructor-facing dashboards for problem-based learning, project-based learning, collaborative argumentation, and various team-based learning activities. We explain key concepts such as group awareness, shared mental models, and group cognition, and review tools including shared mirroring systems, ambient displays, and learning dashboards. We then identify opportunities and challenges in the burgeoning field of learning analytics dashboards for computer-supported collaborative learning and argue that learning dashboards can be a useful aid in facilitating collaborative learning but only when designed with a clear pedagogical purpose informed by research and theory will learning dashboards be able to foster effective teaching and learning strategies.

The cognitive skill of argumentation has two fundamental roles in education. First, it is a curricular goal in and of itself (“learn to argue”), and second, it is an instructional strategy for advancing understanding in history, mathematics, science, and many other subjects (“argue to learn”). In this chapter we inquire how educational technologies can advance these interdependent and mutually supportive roles of argumentation. Our focus is on cognitive tools learners can use to generate arguments more complex than those they would otherwise be capable of. We introduce the ideas of cognitive schemas, cognitive tools, argument tagging, and argument maps; and we discuss our research on the benefits of using argumentation-oriented study tools. Finally, we consider how such tools might be introduced across school subjects.

Collaborative discussions should engage all students, not just a few who dominate (“leaders”) while others participate as “followers” (Zhu, 2006). Cunningham (1991) noted that collaborating learners bring, discuss and debate multiple perspectives to develop their own position while acknowledging others' views. Higher levels of knowledge construction emerged when posts stimulated frequent reply by multiple participants (Aviv, Erlich, Ravid, & Geva, 2003) and were strongly content- and task-oriented (Rovai, 2007). So, to help students more actively and productively engage in knowledge-constructing discussions, an instructor needs to detect students' posts that do not stimulate replies, identify content those posts introduce, and guide students to revise posts to encourage peers' responses. However, such monitoring would be very time- and energy-consuming, especially in large-enrolment courses (Hura, 2010). To set a stage for developing a classifier to automate these tasks, we proposed 10 rhetorical moves characteristic of the interactive mode of Chi and Wylie's ICAP framework (2014) and categorized fine-grained content in discussion posts using these moves. We then identified attributes of posts that triggered a greater number of responses. Rhetorical moves of “asking questions,” “requesting justification,” “building-on,” “giving a reason” and “making a claim” triggered more peer responses. Posts with moves of “disagreeing,” “comparing” and “making claims” predicted students' achievement on a test and an argumentative writing task. We propose analytics for learners and instructors about forming and revising posts to promote constructive discussions and subsequent achievements.

The purpose of this study was to investigate whether achievement motivations influence the adoption of learning strategies and learning strategies influence studying behavior in an online learning environment. The Goal Orientation Questionnaire was used to measure achievement motives, and the Motivated Strategies for Learning Questionnaire was used to assess learning strategies. In addition, data on how learners tagged and annotated the learning materials were collected using software designed to aid studying and to examine studying behavior. Confirmatory factor analyses were conducted on Goal Orientation Questionnaire data from 170 university students who used the learning software to study a chapter from a textbook. Results showed that task value and effort regulation subscales from the Motivated Strategies for Learning Questionnaire positively predicted the number of notes created. In addition, mastery and performance goals positively predicted task value, and work-avoidance goals negatively predicted effort regulation and task value.

Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.

Learners working on major learning projects, such as an undergraduate thesis, frequently engage in information problem solving (IPS). In round-trip IPS, learners set goals and develop a work plan, search for and filter sources, critically analyze and mine key information, and draft and revise a final product. Information problem solving is a prime site for self-regulated learning (SRL) whereby learners formulate and carry out self-designed experiments to improve IPS skills and expand knowledge about the topic of the learning project. We describe nStudy, a software system developed to gather ambient trace data that operationally define features of IPS and SRL as learners work on learning projects. We illustrate how trace data can be used to promote learners’ (a) understanding of the topic of a learning project and (b) development of IPS by generating learning analytics, guidance in the form of quantitative and qualitative accounts describing information learners work with and operations they apply to information. Three main challenges are addressed: learning how to plan a learning project, expanding knowledge of the topic of a learning project, and benefiting from and productively contributing to peer reviews of draft products. We conjecture about an emerging ecology for IPS in which big data and learning analytics can be major resources for education.

Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students’ decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.

An animated concept map represents verbal information in a node-link diagram that changes over time. The goals of the experiment were to evaluate the instructional effects of presenting an animated concept map concurrently with semantically equivalent spoken narration. The study used a 2 × 2 factorial design in which an animation factor (animated vs. static) was crossed with a representation factor (concept map vs. text). Students (N = 140) were randomly assigned to study one of four presentations on the human nervous system. The dependent measures were tests of free recall, knowledge and transfer. The concept map groups significantly outperformed the text groups on free recall and transfer. The animated concept map group did not significantly outperform the static map group. The authors hypothesize that the animated concept map provided no advantage over the static concept map because participants in both conditions were able to use the spoken narrative to sequence their reading.

In some instructional situations, such as foreign language dictation, the degree of correctness of a student's text response can be determined without reference to grammar and semantics by comparison with a target string provided by a course author. The standard sequence comparison procedure, which assesses the distance between two strings in terms of edit costs, makes demands on machine time proportional to the product of the string lengths. This characteristic renders it impractical for real-time correction of multi-word responses on current instructional computer systems. We present a much faster but nonadmissible version of other applications, thereby bringing the technique within range of current microcomputers. The usual method for generating markup for single word responses does not generalize well to multi-word responses because it fails to recognize word boundaries, and will sometimes suggest edits that seem unnatural to users. We propose an extension which attends to word boundaries and thereby recommends corrections that appear more reasonable.

Verbal redundancy arises from the concurrent presentation of text and verbatim speech. To inform theories of multimedia learning that guide the design of educational materials, a meta-analysis was conducted to investigate the effects of spoken-only, written-only, and spoken--written presentations on learning retention and transfer. After an extensive search for experimental studies meeting specified inclusion criteria, data from 57 independent studies were extracted. Most of the research participants were postsecondary students. Overall, this meta-analysis revealed that outcomes comparing spoken--written and written-only presentations did not differ, but students who learned from spoken--written presentations outperformed those who learned from spoken-only presentations. This effect was dependent on learners' prior knowledge, pacing of presentation, and inclusion of animation or diagrams. Specifically, the advantages of spoken--written presentations over spoken-only presentations were found for low prior knowledge learners, system-paced learning materials, and picture-free materials. In comparison with verbatim, spoken--written presentations, presentations displaying key terms extracted from spoken narrations were associated with better learning outcomes and accounted for much of the advantage of spoken--written over spoken-only presentations. These findings have significant implications for the design of multimedia materials.

An animated concept map is a presentation of a network diagram in which nodes and links are sequentially added or modified. An experiment compared learning from animated concept maps and text by randomly assigning 133 undergraduates to study 1 of 4 narrated animations presenting semantically equivalent information accompanied by identical audio narration. Two of the animations presented text; one with concurrent audio and another with delayed audio. Two of the animations presented concept maps; one in black and white and the other with nodes colored to represent semantic relatedness. The concept map groups outperformed the text groups on free recall (p < .05). The black-and-white concept map group outperformed the text groups on a multiple-choice knowledge test (p < .05). No advantages were statistically detected for color enhancements of the animated map. The results indicate that verbal information can be effectively communicated by learner-paced animated concept maps accompanied by audio narrations.

Data were gathered from 248 secondary students (14 years old, 56% female) who learned about the circulatory system in 77 self-selected collaborative groups. The learning outcomes of biology students who summarised by collaborative concept mapping were compared with those of students who summarised by collaborative writing. Learning groups randomly assigned to construct concept maps instead of conventional summaries generated more relations in the summary task and their members obtained higher individual scores on a post-test. The concept mapping strategy was found to be advantageous only for students whose cognitive ability was below the median for the sample and who were placed in groups with other students having low cognitive ability.

The psychometric properties of scores from the Achievement Goal Questionnaire were examined in samples of Japanese (N = 326) and Canadian (N = 307) postsecondary students. Previous research found evidence of a four-factor structure of achievement goals in U.S. samples. Using confirmatory factor-analytic techniques, the authors found strong evidence for the four-factor structure of achievement goals in both the Canadian and Japanese populations. Subsequent multigroup structural equation modeling indicated the metric invariance of this four-factor structure across the two populations.

This exploratory case study examined in depth the studying activities of eight students across two studying episodes, and compared traces of actual studying activities to self-reports of self-regulated learning. Students participated in a 2-hour activity using our gStudy software to complete a course assignment. We used log file data to construct profiles of self-regulated learning activity in four ways: (a) frequency of studying events, (b) patterns of studying activity, (c) timing and sequencing of events, and (d) content analyses of students’ notes and summaries. Findings indicate that students’ self-reports may not calibrate to actual studying activity. Analyses of log file traces of studying activities provide important information for defining strategies and sequences of fine-grained studying actions. We contrast these analytic methods and illustrate how trace-based profiles of students’ self-regulated studying inform models of metacognitive monitoring, evaluation, and self-regulated adaptation.

Automatic question generation from text has been used and adapted to online and self-directed learning platforms. We incorporate methods into the automatic question generation process that are designed to improve question quality by aligning them to the specified pedagogical goals and to a learner's model. This is achieved by extracting, ranking and filtering relevant sentences in the given learning document as well as the questions automatically generated by their semantic associations to the learner model and instructor goals. We propose evaluation techniques for assessing the quality of the questions generated using both human and automatic evaluation.

Links between students' achievement goal orientations and learning tactics were investigated using software (gStudy) that supports a variety of learning tactics and strategies. An achievement goal questionnaire was administered to 307 students enrolled in an introductory educational psychology course. Data tracing study tactics were logged for 80 of these students who prepared for a test by studying a textbook chapter presented as a multimedia document. Using correlations and canonical correlations, we found relationships between goal orientations and activity traces indicating different forms of cognitive engagement. Notably, mastery goal orientation (approach or avoidance) was negatively related to amount of highlighting, a study tactic that is theorized to be less effective than summarizing and other forms of elaborative annotation for assembling and integrating knowledge.

This meta-analysis reviews experimental and quasi-experimental studies in which students learned by constructing, modifying, or viewing node-link diagrams. Following an exhaustive search for studies meeting specified design criteria, 67 standardized mean difference effect sizes were extracted from 55 studies involving 5,818 participants. Students at levels ranging from Grade 4 to postsecondary used concept maps to learn in domains such as science, psychology, statistics, and nursing. Posttests measured recall and transfer. Across several instructional conditions, settings, and methodological features, the use of concept maps was associated with increased knowledge retention. Mean effect sizes varied from small to large depending on how concept maps were used and on the type of comparison treatment. Significant heterogeneity was found in most subsets.

The emergence of large repositories of web-based learning resources has increased the need for valid and usable evaluation tools. This paper reviews current approaches to learning object evaluation and introduces eLera, a set of web-based tools we have developed for communities of teachers, learners, instructional designers and developers. Compatible with current metadata standards, eLera provides a learning object review instrument (LORI) and other features supporting collaborative evaluation. eLera provides limited translation of evaluations and subject taxonomies across communities using different languages and terminology. eLera is designed to assist researchers to gather data on evaluation processes and has been used to teach educators how to assess the quality of multimedia learning resources.

While reviews abound on theoretical topics in educational psychology, it is rare that we examine our field's instrumentation development, and what effects this has on educational psychology's evolution. To repair this gap, this paper investigates and reveals the implications of software technologies for researching and theorizing about core issues in educational psychology. From a set of approximately 1,500 articles published between 1999 and 2004, we sampled illustrative studies and organized them into four broad themes: (a) innovative ways to operationalize variables, (b) the changing nature of instructional interventions, (c) new fields of research in educational psychology, and (d) new constructs to be examined. In each area, we identify novel uses of these technologies and suggest how they may advance, and, in some instances, reshape theory and methodology. Overall, we demonstrate that software technologies hold significant potential to elaborate research in the field.

Learning objects offer increased ability to share learning resources so that system-wide production costs can be reduced. But how can users select from a set of similar learning objects in a repository and be assured of quality? This article reviews recent developments in the establishment of learning object repositories and metadata standards, and presents a formative reliability analysis of an online, collaborative method for evaluating quality of learning objects. The method uses a 10-item Learning Object Review Instrument (LORI) within a Convergent Participation evaluation model that brings together instructional designers, media developers, and instructors. The inter-rater reliability analysis of 12 raters evaluating eight learning objects identified specific items in LORI that require further development. Overall, the collaborative process substantially increased the reliability and validity of aggregate learning object ratings. The study concludes with specific recommendations including changes to LORI items, a rater training process, and requirements for selecting an evaluation team.

A meta-analysis on the effectiveness of Intelligent Tutoring Systems (ITS) in computer science education compared the learning outcomes of ITS and non-ITS instruction. A search of the literature found 22 effect sizes (involving 1,447 participants) that met the pre-defined inclusion criteria. Although most of the ITS were used to teach programming, other topics such as database design and computer literacy were also represented. There was a significant overall effect size favoring the use of ITS. There was a significant advantage of ITS over teacher-led classroom instruction and non-ITS computer-based instruction. ITS were more effective than the instructional methods to which they were compared regardless of whether they modeled misconceptions and regardless of whether they were the primary means of instruction or were an integrated component of learning activities that included other means of instruction.

Models of self-regulate d learning hypothesize that learners selectively match study tactics to varying tasks and diverse goals. In this study, relative to each of 3 contexts—reading for learning, completing a brief essay, and studying for an exam—students rated the frequency with which they applied 26 study tactics, used 20 textbook features and other resources, and adopted 30 goals for studying. Analyses revealed substantial context effects in these self-reports. Nine separate principal component analyses of ratings corresponding to cells in a 3 X 3 matrix of (a) tactics, resources, and goals by (b) contexts, identified considerable discrepancies in items' assignment to components, and heterogeneous loadings across contexts. These findings bolster the premise that students' reports of self-regulati ng studying behaviors are context specific. They also raise questions about using self-reports of self-regulate d learning that do not reflect context effects. Strategic learners have four characteristics. First, they critically assess tasks, such as studying a textbook chapter, to identify features that may influence how they engage with the task and the degree of success they will have. Second, on the basis of their assessment, strategic students define short-term goals and probably overall goals for studying. Third, they know alternative cognitive tactics that provide options about tactics to apply to studying. Finally, strategic students make judgments about which tactic(s) or pattern(s) of tactics has the greatest utility for achieving the goals they choose to pursue (Hadwin & Winne, 1996; Winne, 1995, 1997; Winne & Hadwin, 1998). Goals provide standards against which strategic students may monitor unfolding engagement with the task or the product(s) constructed as they engage with it. When strategic students monitor these events, they are self-regulating learning (SRL; Winne, 1995). SRL updates self-knowledge and perceptions about the task's changing states, thereby creating information that selfregulating learners can use to select, adapt, and even generate tactics (Butler & Winne, 1995; Hadwin & Winne, 1997; Winne & Hadwin, 1998). The element of intent to adapt cognitive engagement distinguishes SRL from "just using" tactics. An expert whose domain knowledge includes well-formed, automated tactics that

This chapter reviews research about the collaborative use of concept maps for learning. Although decades of research have produced some understanding of the cognitive effects of using concept maps in individual learning, theories about their effects in collaborative learning are less firmly established. The review incorporated a systematic literature search, analysis of dependent variables as effect sizes, and discussion of representative studies. Students who learned collaboratively by constructing concept maps outperformed those who learned from other activities such as studying texts, outlines, lists and lectures. However, no effect of studying pre-constructed concept maps in collaborative settings was statistically detected. There was homogeneity across the subsets of studies investigated. The review concludes with suggestions for future research in learning with concept maps in collaborative environments.

Educational psychology has generated a prolific array of findings about factors that influence and correlate with academic achievement. We review select findings from this voluminous literature and identify two domains of psychology: heuristics that describe generic relations between instructional designs and learning, which we call the psychology of “the way things are,” and findings about metacognition and self-regulated learning that demonstrate learners selectively apply and change their use of those heuristics, which we call the psychology of “the way learners make things.” Distinguishing these domains highlights a need to marry two approaches to research methodology: the classical approach, which we describe as snapshot, bookend, between-group experimentation; and a microgenetic approach that traces proximal cause-effect bonds over time to validate theoretical accounts of how learning generates achievements. We argue for fusing these methods to advance a validated psychology of academic achievement.

The Learning Object Review Instrument (LORI) is an evaluation framework designed to support collaborative critique of multimedia learning resources. In this chapter, the interactions among reviewers using LORI are framed as a form of collaborative argumentation. Research on collaborative evaluation of learning resources has found that reviewers’ quality ratings tend to converge as a result of their interactions. Also, novice instructional designers have reported that collaborative evaluation is valuable preparation for undertaking resource design projects. The authors reason that collaborative evaluation is effective as a professional development method to the degree that it sustains argumentation about the application of evidence-based design principles.

The power of Logo derives from the fact that, like Lisp, it is a functional programming language. But, unlike Lisp, Logo permits special syntactic features to make programs easier to read and write. From the point of view of the Logo interpreter these special features require separate treatment, which has tended to result in rather cumbersome implementations. However, the concepts of the procedure object and syntactic sugar can be used to greatly simplify the design of the interpreter. In this paper, it is shown how a procedure can be defined using a procedure object and how syntactic sugar forms a bridge between conventional Logo syntax and the new procedure definition. A mechanism for higher-order programming is also described, which provides a powerful means for the expression of algorithms.

Instructional systems that accept text responses entered by the learner must be capable of dealing with the inevitable occurrence of misspellings. A widely known procedure which finds the edit distance between two strings, and has been found effective in recognizing misspellings, can be easily extended to also annotate the response to give the learner feedback on the precise nature of the error. This paper considers the instructional value of text markup and shows how to extract markup information from the matrix normally generated in the calculation of edit distance. Included is a listing of a short Pascal program that illustrates the main concepts discussed.

The unceasing growth of the Internet has led to new modes of learning in which learners routinely interact on-line with instructors, other students, and digital resources. Much recent research has focused on building infrastructure for these activities, especially to facilitate searching, filtering, and recommending on-line resources known as learning objects. Although newly defined standards for learning object metadata are expected to greatly improve searching and filtering capabilities, learners, instructors, and instructional developers may still be faced with choosing from many pages of object listings returned from a single learning object query. The listed objects tend to vary widely in quality. With current metadata and search methods, those who search for learning objects waste time and effort groping through overwhelming masses of information, often finding only poorly designed and developed instructional materials. Hence, there is a clear need for quality evaluations prior to making a recommendation that can be communicated in a coherent, standardized format to measure the quality of learning objects.

Working from the premise that students need advanced self-regulated learning (SRL) skills to succeed in e-learning environments, this chapter describes the use of a software application (gStudy) designed to help students take control of their learning and become better self-regulated learners. To address the challenges educators face in developing students’ metacognitive monitoring and self-regulatory skills, gStudy’s cognitive tools were designed in accordance with current SRL theory. Undergraduate students who used gStudy in an educational psychology course commented that they appreciated gStudy’s features, interface, and ability to positively influence their approach to learning. The authors conclude that SRLfostering software applications such as gStudy may be key strategic elements in institutional transitions to e-learning. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB13416 INFORMATION SCIENCE PUBLISHING This chapter appears in the book, Making the Transition to E-Learning: Strategies and Issues edited by Mark Bullen and Diane Janes © 2007, Idea Group Inc. Cognitive Tools for Self-Regulated E-Learning 301 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Cognitive Tools for Self-Regulated E-Learning Greater access to information and a growing need for lifelong learning have increased the importance of self-regulated learning (SRL) research for postsecondary education (Narciss & Korndle, 1998). More than ever before, students are learning outside regular classrooms, often in online learning environments that require different skills from those needed in on-campus lectures. With virtually unlimited access to information, students must take more active roles in evaluating the quality and relevance of the information available to them and in assessing their understanding of that information (Nesbit & Winne, 2003). This transition brings with it a definite shift in the roles of teachers and of students. While teachers will still be responsible for establishing clear goals and objectives and for guiding students with feedback, there will be greater onus on students to assess whether the strategies and tactics they choose really will help them to meet their educational goals. Although the need for individuals to take responsibility for their learning is growing, students often fail in monitoring whether they are meeting course requirements or advancing toward their goals (Schunk & Ertmer, 2000; Winne & Hadwin, 1998; Zimmerman, 2002). Cognitive toolsets that help students to become better at monitoring and adapting their learning strategies offer a potential solution to this increased need for SRL in formal coursework and in lifelong learning (Brown, Hedberg, & Harper, 1994). Institutions that can seize this opportunity to produce graduates with strong SRL skills will be recognized as having successfully met the changing demands of education. Accordingly, this chapter focuses on a software application designed to help students take control of their learning and become better self-regulators. After providing a brief account of SRL theory, we introduce gStudy, a set of cognitive tools developed at Simon Fraser University to support SRL. Throughout, we look at gStudy both as a practical tool that educators can use in their courses to help students and as a research tool that researchers can use to learn more about the theories underlying SRL and their applications. We conclude by evaluating the significance of cognitive tools for SRL and applications such as gStudy in the context of institutional transitions to e-learning. Theoretical Background Self-regulation of learning includes analyzing learning tasks; setting goals; identifying and choosing appropriate strategies for achieving the goals; enacting 16 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/cognitive-tools-self-regulatedlearning/25627

As differing evaluation instruments are adopted in learning object repositories serving specialized communities of users, what methods can be adopted for translating evaluative data across instruments in order to share this data among different repositories? How can evaluation from different reviewers be properly integrated? How can explicit and implicit measures of preference and quality be combined to recommend objects to users? In this research we studied the application of Bayesian belief network (BBN) to the problem of insufficient and incomplete reviews during learning objects evaluation, and translating and integrating data among different quality evaluation instruments and measures. Two BBNs were constructed to probabilistically model relationships among different roles of reviewers as well as among items of different evaluation measurements. Initial testing using hypothetic data showed that the model was able to make potentially useful inferences about different dimensions of learning object quality. We further extend our model over geographic distances assuming that the reviewers would be distributed and that each reviewer would change the underlying BBN network (to a certain extent) to suit his/her expertise. We highlight issues that arise due to a highly distributed and personalized BBN network that can be used to make valid inferences about learning object quality.

Effects of self-regulated learning (SRL) have been investigated in a variety of contexts. In this research, we explore advanced learning technologies based on the information processing model of SRL in the context of structured programming. We conducted an experiment to study ways to enhance the use of programmers' working memory, to develop tactics to carry out task level activities during programming, and to learn how to program more effectively. The results of the experiment indicate that programmers who received SRL-based treatment outperformed programmers who did not receive the treatment. We argue that the infusion of SRL-based technological interfaces would have a positive influence on the performances in programming.

This study examined the perceived effectiveness of a collaborative evaluation process for teaching qualitative aspects of learning object design in a distance education course at Athabasca University in Alberta, Canada. Working in groups of four, 24 distance students in a graduate level instructional design course participated in two 2-hour audio conferences, using a convergent participation model for the evaluation of learning objects. After the first conference, which featured study and application of the nine criteria of the Learning Object Review Instrument (LORI), the participants independently rated a set of learning objects. In the second audio conference they reconvened to compare and discuss their ratings. Six to nine months later the participants reported favorable outcomes from the experience in their understanding of learning objects, and confidence in determining learning object quality. In judging the impact on their subsequent design efforts, the most frequent comments indicated that the convergent participation experience provided both an appreciation of the complexity of learning object design and a method for judging their quality.; Cette étude porte sur la perception de l'efficacité d'une méthode d'évaluation collaborative d'objets d'apprentissage pour enseigner des critères de qualité de ces objets dans le contexte d'un cours à distance à l'Université Athabasca en Alberta, Canada. Vingt-quatre étudiants ont participé, en équipes de quatre, à deux conférences téléphoniques, au cours desquelles ils ont appliqué un modèle de participation convergente. À la suite d'une première conférence portant sur les neuf critères de l'instrument utilisé pour l'évaluation des objets d'apprentissage (LORI), chaque participant a effectué une évaluation individuelle d'une sélection d'objets. Lors d'une deuxième conférence, ils ont partagé leurs évaluations et commentaires. Six à neuf mois plus tard, les participants ont répondu à un questionnaire visant à évaluer notamment leur compréhension des objets d'apprentissage et leur habileté à les évaluer. Les perceptions des étudiants à cet égard s'avèrent positives. Les répondants ont également noté un impact appréciable de cette expérience sur leur pratique professionnelle. Leurs commentaires indiquent que cette expérience leur a permis de mieux apprécier la complexité du processus de conception des objets d'apprentissage et les a outillés d'une méthode adéquate pour en évaluer leur qualité.

Learning object repositories and evaluation tools have the potential to serve as sites for interaction among different cultures and communities of practice. This paper outlines the web-based learning object evaluation tools we have developed, describes our current efforts to extend those tools to a wider range of user communities, and considers methods for fostering interaction among user communities. The approaches considered include establishing shared but differentiated learning object evaluation standards, mapping between local languages, ontologies and practices, and recommending objects across community boundaries.

A Gagne-style learning hierarchy often permits a large number of alternate linear arrangements (sequences) of instructional objectives. An alternative is described here to traditional methods of choosing between sequences. Its premise is that, for every sequence, a value temed thememory load can be calculated which is theoretically related to the probability that students will fail to recall prerequisite objectives. A graph theoretic approach is taken in presenting an algorithm which generates a minimal memory load sequence from a learning tree, a restricted but frequently encountered type of learning hierarchy. In order to assess the effectiveness of the algorithm in generating low memory load sequences when given hierarchies which are not trees, it was applied to several published examples of learning hierarchies. The results indicated that the algorithm is effective as an heuristic, especially when combined with a hill-descending procedure which attempts to incrementally improve the generated sequence.

In the 1st of 2 experiments on spatial ability, groups of sighted and blind, light-reared (LR) and dark-reared (DR) rats were tested on a series of (Hebb-Williams) maze problems and their reversals under appetitive and aversive reinforcement conditions. Significant effects due to early rearing conditions, vision at time of testing, and problem were found. Dark-reared rats learned the problems whose solution depended on nonvisual cues more slowly than LR animals. Blindness at time of testing had a significantly adverse effect on the performance of LR and DR rats on all problems, but a significantly greater effect in the DR animals. In a 2nd experiment DR rats were also found to perform less effectively than LR rats on a 17-arm radial maze throughout a 36-day period during which variations in the task were introduced. The results reveal the impact of early visual experience on the development of the ability to acquire spatial concepts.