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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.