Tracing code execution and creating memory diagrams have long been tools for teaching computer science. These techniques are useful beyond demonstration or assessment of understanding: they are teaching and learning tools. We argue that if consistent diagramming is used across courses and programming languages, variations to the level of detail in those diagrams can become a teaching technique. We previously described a consistent form of memory diagram to be used across curricula, Ithaca Memory Diagrams IMDs, and explained their basic use. After four more years of classroom use, we find that variation in these diagrams is useful to emphasize specific concepts and highlight features of various languages. Our updates to previously published memory diagrams improve their usefulness in courses from the high-level abstraction of CS1 to the depth of systems courses. We provide specific examples of this variation with explanation of the intended benefits.

This paper extends a previous publication describing a system that utilizes a wide variety of available assessment information to automatically analyze students’ understanding at a conceptual level and offer relevant automated support to teachers and students. This organization and support can be at the course level or at the level of curriculum for an entire program of study. Intelligent support includes interactive visualization of the conceptual knowledge assessment, individualized suggestions for resources, and suggestions for student groups based on conceptual knowledge assessment. This system differs from prior related work in that it can operate on entire program curricula, and that the basis for analysis and feedback is entirely customized to the individual instructors’ course content. We discuss how the system is configured for courses and the curriculum levels, and describe our experience that indicates the benefits of the approach. We then provide detailed descriptions of how the system performs analysis and offers support in both course and curriculum scenarios.

This paper provides an overview of the five posters accepted for the EduPar'19 poster session. The poster session has proved to be an important opportunity for interaction among the community, fostering the discussion of innovative approaches and ideas that are under development.

We present a system that utilizes a wide variety of available assessment information to automatically analyze students' understanding at a conceptual level and offer relevant automated support to teachers and students. This support includes interactive visualization of the conceptual knowledge assessment, individualized suggestions for resources to improve areas of weakness, and suggestions for dynamic student groups for in-class activities. This system differs from prior related work in that the basis for analysis and feedback is entirely customized to the individual instructors' course content. We discuss how the system is configured for each course, and provide evidence that this configuration process helps instructors improve their course content. We then provide detailed descriptions of how the system performs analysis and offers support including suggesting resources for students and creating dynamic groups within a class. Finally, we discuss the potential benefits provided by this system and how the system is being applied to six different computer science courses currently.

This lightning talk describes our current effort to create a system that helps teachers organize the content of their computer science courses while simultaneously providing a basis for intelligent support. This work blends the disciplines of computer science education, artificial intelligence in education, and instructional design to create a holistic system that helps teachers create a unified vision of their course from diverse learning resources and assessment techniques. The vision is created in the form of a concept map with links to external materials and assessments (including traditional materials like textbooks and exams, and more advanced technology like online interactive practice environments). We are creating these concept maps for our computer science curriculum at Ithaca College and we have found clear benefits to organization and content. Beyond these improvements to courses, we seek to use the resulting concept map to offer intelligent support for students and instructors. Students can benefit by seeing their assessment automatically summarized by concept rather than by assignment, and receive suggestions of materials crucial to their understanding. Instructors can benefit from assessment summaries about individuals/ or entire classes/ understanding of specific concepts. The system can also make recommendations for dynamic groups to be formed for short-term in-class collaboration. Currently we have basic prototypes of this functionality and we/re seeking feedback from others who may have engaged (even informally) in similar techniques, as well as any collaborators who are interested in trying this technique in their courses or integrating their materials with our system.

Collaborative learning can be beneficial for students, but its success is highly dependent on the characteristics of the students grouped together. Structuring group interactions enhances the effectiveness of collaborative learning [1] and therefore the structuring must be done appropriately. Creating successful groups is challenging for instructors, who generally do not have the ability to hand-select groups. We have created a system to dynamically recommend student groups for short-term collaboration. Instructors can request group suggestions based on various criteria related to students' performance on assessment materials. This system creates groups based on different pedagogical theories we find to be salient to group learning.

We argue that authoring of Intelligent Tutoring Systems can be beneficial for instructors that choose to author content, rather than a time-consuming burden as it is often seen. In order to make this a reality, the authoring process must be easy to understand, must provide immediate benefit to the instructor doing the authoring, and must allow for incremental development and improvement. We describe a methodology that meets all of these needs using concept maps as a basis for authoring. The methodology creates a basis for intelligent support that helps authors improve their course organization and content as they work on the authoring task. We also present details of the rapid prototype being developed to apply the methodology and the initial experiences from its use.

Undergraduate teaching assistants have been used in many classes, over many years, and at many institutions. The literature primarily focuses on the practice in a university environment with large classes. We focus instead on the use of undergraduate teaching assistants in the small college, small class environment. We have been employing students in this capacity for over 15 years and have gained some insight on how best to use these undergraduate teaching assistants in the small classroom setting. We believe these conclusions can inform the design of other undergraduate teaching assistant programs.

Online, interactive textbooks utilizing multimedia are continually gaining popularity in computer science courses. We present a system for automated analysis that can harness the power of these textbook and practice systems to provide information about high-level conceptual understanding to educators. The system presents a visualization using data logged by an interactive textbook to provide numeric estimates of a student's knowledge of course concepts. This information can be used to support teachers and individual students. The basis of our system is a Concept Graph, an artifact representing the concepts to be taught during the course, and their interrelations. We describe the manner in which our system uses these Concept Graphs to provide useful information to educators and students.

In this poster, we present a hypothesis involving inherent trade-offs between a user interface designed to promote immersion and a user interface designed to promote efficiency in the context of an Exploratory Learning Environment (ELE). We consider the entire user interface, but also specifically the interface for automated, intelligent coaching. We present an example system that provides both types of interface, and present our plans for experimentation.

Hand-drawn memory diagrams are frequently used in computer science to demonstrate new programming concepts and support students' understanding of program functionality. These diagrams often vary among courses, instructors, and languages, which confuse students moving through the curriculum. Consistent memory diagrams throughout a curriculum not only alleviate confusion but offer a scaffold for students to transfer their understanding between courses taught at different levels of complexity and in different languages. We describe our standardized system for memory diagrams as it is used in our curriculum to demonstrate this scaffolding process through multiple concepts and programming languages.

We describe the design of the Invention Coach, an intelligent, exploratory learning environment (ELE) to support Invention, an exploratory learning activity. Our design is based on a two-pronged approach. Our own study of naturalistic teacher guidance for paper-based Invention uncovered phases in the Invention process. Prior research on the mechanisms of learning with Invention activities revealed specific instructional strategies. These two sources informed the design of the guidance offered by the Invention Coach. To our knowledge, this is the first design of a guided environment for Invention activities inspired by a model of naturalistic teacher guidance. Our work offers insight into styles of guidance that could apply to other exploratory learning environments.

In this paper, we identify Learning to Learn Together (L2L2) as a new and important educational goal. Our view of L2L2 is a substantial extension of Learning to Learn (L2L): L2L2 consists of learning to collaborate to successfully face L2L challenges. It is inseparable from L2L, as it emerges when individuals face problems that are too difficult for them. The togetherness becomes a necessity then. We describe the first cycle of a design-based research study aimed at promoting L2L2. We rely on previous research to identify collective reflection, mutual engagement and peer assessment as possible directions for desirable L2L2 practices. We describe a CSCL tool: the Metafora system that we designed to provide affordances for L2L2. Through three cases in which Metafora was used in classrooms, we describe the practices and mini-culture that actually developed. In all contexts, groups of students engaged either in mathematical problem solving or in scientific inquiry and argumentation. These cases show that L2L2 is a tangible educational goal, and that it was partially attained. We show how the experiments we undertook refined our view of L2L2 and may help in improving further educational practice.

This article describes efforts to offer automated assessment of students within an exploratory learning environment. We present a regression model that estimates student assessments in an ill-defined medical diagnosis tutor called Rashi. We were pleased to find that basic features of a student's solution predicted expert assessment well, particularly when detecting low-achieving students. We also discuss how expert knowledge bases might be leveraged to improve this process. We suggest that developers of exploratory learning environments can leverage this technique with relatively few extensions to a mature system. Finally, we describe the potential to utilize this information to direct teachers' attention towards students in need of help.

: Social networking services (SNS), such as Facebook, are an increasingly important platform for computer supported collaborative learning (CSCL). However, little is known about whether and how academic opinion change and argumentative knowledge construction (AKC) can be facilitated in SNS. Existing argumentation practice in informal SNS discussions typically lacks elaboration and argumentative quality. We investigate the potential benefits of argument structure provided through individual computer-supported argument diagramming to foster academically sound opinions in the context of Facebook. In a quasi-experimental lab study, we found evidence of academic opinion change along with correlations of opinion change with knowledge gains.

Computer-Supported Collaborative Learning (CSCL) has been demonstrated to improve student interaction in complex collaborative learning scenarios. When orchestrated appropriately, it also provides opportunities for learning high-level social learning skills, or “learning to learn together” (L2L2), but these opportunities are often only dealt with implicitly. This paper presents work towards an intelligent system that can scaffold L2L2 across many domains by (a) offering carefully-designed message templates that encourage peers to communicate with their groups about their learning process, (b) analyzing student work and recommending a specific set of these message templates that are pertinent to their moment-by-moment interaction. We present methods by which the system can use automated analysis techniques to recognize opportunities where students might benefit from these messages, and either send the message directly or prioritize message templates for students’ use.

Collaboration in complex learning scenarios does not succeed automatically without structuring the learning process. The Metafora project (http://www.metforaproject.org) is designing a pedagogy and a platform of web-based software to support learning to learn together (L2L2) in the context of math and science. The platform serves both as a toolbox of various learning tools and as a communication architecture to support cross-tool interoperability. The central tool in the Metafora system is a web-based application offering a visual language for planning, enacting and reflecting on learning activities. In the demonstration we will present our pedagogical approach for supporting L2L2 activities and the platform developed on the basis of this understanding. In particular we will demonstrate how the platform can be integrated in successive activities. Learning to Learn Together and the Metafora tool Most knowledge creation is conducted by teams and not by individuals. In addition, learning mediated by the Internet is often focused on learning together with others. It is therefore important that we teach and support the complex competence of learning to learn together (L2L2). While there has been some research on learning how to learn (L2L, e.g., Claxton, 2004; Fredriksson & Hoskins, 2007; Higgins et al., 2006), there has been little research on L2L2. Learning how to learn together implies that all the group members are able to coordinate, regulate and plan the learning task by balancing issues of individual ability, motivation and expectations through constant dialogue. The process of L2L2 can be described and studied by analyzing the groups’ collaborative learning activities and behaviors as a set of sub-skills: distributed leadership, mutual engagement for fulfilling collaborative tasks, a dialogue where students can discuss their ideas and create new ones, and peer group assessment, where members give and accept feedback from each other, routinely reflecting on their work. The Metafora project (http://www.metfora-project.org), funded by the EC, is designing a pedagogy and a platform of web-based software to support L2L2 in the context of math and science. A key technical and pedagogical innovation of the project is to support L2L2 within a group of learners. We present our platform (see Fig. 1), which serves both as a toolbox of various learning tools and as communication architecture to support cross-tool interoperability. The toolbox facet of the system provides a graphical container framework in which the diverse learning tools can be launched and used. Basic functionalities that is globally available are user management (login/logout and group membership for both local groups of students sitting at one computer as well as remote, collaborative groups), a chat system to discuss and organize work between group members, and a help request function that is present across the entire platform. Below we describe in some more detail certain components and features of the Metafora system. The planning/reflection tool The planning/reflection tool offers a visual language that enables students to create and map representations of their work for planning, enacting and reflecting on Metafora learning activities (see Fig. 1). The main feature of this tool is the use of cards and connectors to present a plan for future work or to create a diagram of work completed for reflection. The cards contain visual symbols and titles, as well as space to insert free text (see Fig. 1) The symbols and the titles represent different stages and processes related to inquiry learning (e.g., experimentation, building models, making hypotheses), attitudes taken towards the group work (e.g., being critical, being open) and cards that allow access to different resources within the Metafora tool box (e.g. CSCL 2013 Proceedings Volume 2: Short Papers, Panels, Posters, Demos, & Community Events