The conceptualisation of cognitive tools in learning and teachnology: A review <s> Conclusion <s> It is important, when writing opinion essays, for students to consider and integrate both arguments and counterarguments to develop a final conclusion. In this article, the authors explored the effect of criteria instruction and a graphic organizer to promote integration of arguments and counterarguments. The researchers randomly assigned 84 participants from an undergraduate educational psychology course to 1 of 4 conditions: training only, organizer only, combined, and control. The graphic organizer resulted in more refutations of counterarguments. However, criteria instruction resulted in better integration of argument and counterargument (with stronger rebuttals and more balanced reasoning). The authors discussed how the 2 interventions may have activated somewhat different argumentation schema in students. <s> BIB001 </s> The conceptualisation of cognitive tools in learning and teachnology: A review <s> Conclusion <s> 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. <s> BIB002 </s> The conceptualisation of cognitive tools in learning and teachnology: A review <s> Conclusion <s> 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. <s> BIB003
In this concluding section, we illustrate how the three principles are evident in the design of the DM  and nStudy BIB003 , another learning technology developed at Simon Fraser University. The DM was designed with attention to the key challenges that university students face in preparing well-reasoned arguments . They often ignore counterevidence, and they often fail to present and rebut counterarguments  . When they do acknowledge counterarguments, they may resist argument-counterargument integration such as introducing pertinent qualifications to the thesis BIB001 . Students also have difficulty judging the strength of evidence supporting claims and presenting the components of their argument in a coherent order. Inspired by its predecessor, the argumentation Vee diagram, the right half of the DM is dedicated to counterarguments and counterevidence. The three principles for designing cognitive tools are manifest in the design of the DM. The visual representation prompts students to construct balanced arguments that examine both sides of an issue. Students interact with the DM through several features designed around the key challenges we identified. They horizontally link related pro and con reasons and in so doing gain practice in argument-counterargument integration. The work of reorganising the order of the argument is partially distributed to the tool by a feature which allows students to move a claim (and any opposing claim linked to it) up or down to a new position. Each reason (pro or con) has a slider that students can adjust to represent the strength with which it supports or undermines the central thesis. When it is deployed in undergraduate biology classes, students who constructed arguments using the DM showed growth in the quality of their arguments over a series of three assignments  . Studying educational resources places extraordinary demands on university students because they must simultaneously fulfill the dual tasks of cognitively processing information in multiple resources while strategically self-regulating that processing as they strive to achieve learning goals. nStudy is a collection of web-based cognitive tools designed to scaffold metacognitive monitoring and self-regulated learning for learners studying text, writing essays, or engaged in other forms of text-based information problem-solving . In the upcoming version of nStudy, instructors will be able to create specialised templates that represent schemas they intend learners to acquire. The templates can include text fields and sliders, akin to the palettes and belief meter of Bioworld, which prompt learners to record in the template what they know or believe about a topic they are investigating. The templates students fill in make their understanding of a topic more self-evident and invite the self-regulatory processes of evaluating and revising learning strategies. To fully acquire the schema represented by a template, a student may need to thoughtfully activate and complete a template artefact multiple times and in a variety of contexts. nStudy is currently being augmented with learning analytics designed to motivate and guide students towards productive self-regulated learning   . BIB002 described how learning analytics, beyond simply mirroring learners' behaviour, can visualise students' engagement with resources in ways that represent their learning strategies and foster their metacognition and motivation to learn. Goal setting is a crucial element of self-regulated learning. If students are able to set goals for studying by identifying materials they wish to study, the schedule they wish to follow, and the study tactics (e.g., tagging, note-taking) they wish to deploy, learning analytics can be configured to track and report to students their progress towards those goals. Seen as a cognitive tool for goal setting and goal fulfillment, the learning analytic described by Marzouk et al. exemplifies the three design principles of representation, interaction, and distributed cognition: Key concepts (resources, activities, schedule) and relationships among them are represented in the visualisation; the clerical task of gathering and aggregating trace data is distributed to the tool; and the students' repeated interactions with the tool are designed to help them internalise fundamental concepts and processes of self-regulated learning. Because they can be operationally defined, the three attributes of representation, interactivity, and distributed cognition that characterise software-based cognitive tools also have a role to play in evaluating, iteratively redesigning, and researching the tools. Data gathered by means of log files, screen capture, eye tracking, and student products can be analysed to evaluate whether features of the tools are having their intended effects. To evaluate the interactivity of the DM interface, one could analyse screen captures and other trace data to determine whether, how often, and under which circumstances students are using the slider to estimate the strength of reasons. To evaluate distributed cognition, one could analyse eye-tracking data to investigate the extent to which students visually refer back to the DM as they search sources for evidence. To evaluate whether the representational structure of the DM has been internalised, one could look for its traces in the structure of essays students write later without the aid of the DM. Being derived from theory and amenable to operational definition, the three attributes we have described are suitable for guiding both hypotheses and methods in research on how cognitive tools can be designed to promote learning.