Creating humorous content has been shown to improve an individual’s emotional well-being by decreasing stress, overcoming anxiety, and enhancing interpersonal relationships. However, it is common knowledge that a good sense of humor is not common. In this paper, we propose a natural language processing (NLP) driven collaborative tool based on appropriate incongruity theory to assist novices in writing humorous content. We use cartoon-caption writing as the use case since it is a popular method where people engage in creating humorous content. The paper describes the design of our co-authoring tool and findings from a two-part user study where (1) 20 participants used our tool to co-author cartoon captions and (2) 66 participants evaluated those captions. Our findings show that the tool helped participants to identify incongruous visual elements in the cartoon, support ideation, and expand the narrative. This resulted in co-authored captions more frequently rated funnier than those written without the tool. This approach can be appropriated to other humor generation applications including creative writing, creating memes, sketch comedy, and advertising.

For many people, social media is an important way to consume news on important topics like health. Unfortunately, some influential health news is misinformation because it is based on retracted scientific work. Ours is the first work to explore how people can understand this form of misinformation and how an augmented social media interface can enable them to make use of information about retraction. We report a between-subjects think-aloud study with 44 participants, where the experimental group used our augmented interface. Our results indicate that this helped them consider retraction when judging the credibility of news. Our key contributions are foundational insights for tackling the problem, revealing the interplay between people's understanding of scientific retraction, their prior beliefs about a topic and the way they use a social media interface that provides access to retraction information.

Accessibility can be overlooked in the Design-phase in creating digital products. This can lead to increased costs when problems are discovered later in product development or deployment. Our work aimed to discover how well the 167 W3C accessibility evaluation tools support the Design-phase. Using Grounded Theory, we identified key characteristics of the tools and their support. We found that just 30 (18%) of the tools support the Design-phase; by contrast, 128 (76.5%) support the Later-phases. Of the 30 tools supporting the Design-phase, 25 (83%) support color checks but few support the other W3C basic design considerations. Our key contributions are: (1) our comprehensive study of the 167 W3C accessibility evaluation tools; (2) our insights about their support for the Design-phase; (3) recommendations for improved support for accessibility in the Design-phase.

Information retrieval systems are a ubiquitous part of people's daily lives. The personalised services of such systems are driven by user models that are typically built from data collected from large numbers of people. There is a growing demand that we build such "intelligent systems" in ways that ensure the user can understand and control them. This is the spirit of legislation across many countries, such as the European Union General Data protection Regulation (GDPR). Essentially, this reflects people's demand to control their own data and its use.

Many universities are introducing a new major in Data Science into their offering, to reflect the explosive growth in this field and the career opportunities it provides. As a field Data Science has elements from Computer Science and from Statistics, and curricula plans differ widely, both in the balance between the CS and Stats aspects, and also in the emphasis within the computing topics. This paper reports on the curriculum that has been taught for three years now at the University of Sydney. In particular, we describe the approach of a sequence of computing subjects which were developed specifically for the major, in order to bring students over several years to a sophisticated understanding of the data-handling aspects of Data Science. Students also take traditional subjects from both CS (such as Data Structures or AI) and from Statistics (such as Learning from Data and Statistical Inference). The data-centric specially-designed subjects we discuss in this paper are (i) Informatics: Data and Computation (in the first year), (ii) Big Data and Data Diversity (in the second year), and then upper-division subjects on (iii) Data Science Platforms, and (iv) Human-in-the-Loop Data Analytics.

Public interactive displays (PID) are a promising technology for providing information and collecting feedback in public spaces. Research on PIDs has shown that, like all public displays, their efficacy is reduced by display blindness. Rather than increase the visual attention-grabbing nature of PIDs, we propose that additional understanding is required around how and when these displays are able to offer value to users. We tackle this through a systematic analysis of PID studies published in the literature, which led to 9 aspects of value across 4 factors: people, location, community, and time. We discuss the identified aspects and their utility for the design of PIDs through a review of our own deployments carried out by 4 different labs across 5 countries. We conclude with a set of recommendations for identifying and optimising the intended value of future PIDs.

Many people, especially those in sedentary occupations, fail to achieve the recommended levels of physical activity. Virtual reality (VR) games have the potential to overcome this because they are fun and also can be physically demanding. This paper explores whether a VR game studio can help workers in sedentary jobs to get valuable levels of exercise. We studied how 11 participants used our VR game studio in a sedentary workplace over 8-weeks and their perceptions of the experience. We analysed the physical exertion in the VR game studio, comparing this to their step counts from a smartwatch. All participants achieved valuable levels of physical activity and mood benefits. Importantly, for 6 participants, only with the VR game studio did they meet recommended activity levels. Our key contributions are insights about the use of a workplace VR game studio and its health benefits.

As self-tracking has evolved from a niche movement to a mass-market phenomenon, it has become possible for people to track a broad range of activities and vital parameters over years, even decades. The associated opportunities, as well as the challenges, have had very little research attention so far. With the phenomenon of long-term tracking becoming widespread and important, we have identified its key characteristics by drawing on work from UbiComp, HCI, and health informatics. We identify important differences between long- and short-term tracking, and discuss consequences for the tracking process. Going beyond previous models for short-term tracking, we now present a model for long-term tracking, integrating its distinctive characteristics in purposeful and incidental tracking. Finally, we present major topics for future research.

Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations.

This paper aims to link student facing Learning Analytics Dashboards (LADs) to the corpus of research on Open Learner Models (OLMs), as both have similar goals. We conducted a systematic review of literature on OLMs and compared the results with a previously conducted review of LADs for learners in terms of (i) data use and modelling, (ii) key publication venues, (iii) authors and articles, (iv) key themes, and (v) system evaluation. We highlight the similarities and differences between the research on LADs and OLMs. Our key contribution is a bridge between these two areas as a foundation for building upon the strengths of each. We report the following key results from the review: in reports of new OLMs, almost 60% are based on a single type of data; 33% use behavioral metrics; 39% support input from the user; 37% have complex models; and just 6% involve multiple applications. Key associated themes include intelligent tutoring systems, learning analytics, and self-regulated learning. Notably, compared with LADs, OLM research is more likely to be interactive (81% of papers compared with 31% for LADs), report evaluations (76% versus 59%), use assessment data (100% versus 37%), provide a comparison standard for students (52% versus 38%), but less likely to use behavioral metrics, or resource use data (33% against 75% for LADs). In OLM work, there was a heightened focus on learner control and access to their own data.

An important role of open learner models (OLMs) is to support self-reflection. We explore how to do this for an OLM based on fine-grained long term physical activity tracker data that many people are accumulating. We aim to tackle two well-documented challenges that people face, in making effective use of an OLM for reflection. 1. We created a tutorial to scaffold sense-making needed to understand the meaning of the OLM. 2. We integrated an interface scaffold to help users consider key questions for effective reflection. We report the results of a qualitative think-aloud lab study with 21 participants viewing their own long term OLM. To evaluate the tutorial scaffolding, we split participants into an experimental group, who did a tutorial before exploring the OLM and a control group which explored the interface without the tutorial. To evaluate the reflection scaffolding, all participants first explored the interface as they wished. We then provided goal prompts to scaffold reflection. Our study revealed that, under lab conditions, the tutorial scaffolding was not needed - all participants in both groups could readily understand the OLM. However, we found that several of the goal prompts were important to help participants consider key questions for effective reflection. Our key contribution is insights into the design of scaffolding for reflection in a life-long learning context of gaining insights and setting goals for physical activity.

The Internet-of-Things (IoT) consists of a large number of interconnected, low cost devices and the framework for managing them. While this provides the means for rich and ubiquitous personalized interaction, a key gap is the lack of support for user modeling to harness and manage personal data gathered from IoT. Our work fills this gap by creating the IoTum user modeling framework for Internet-of-Things applications. Our design goals were to make it easy for IoT application developers to use, tackling the difficulty of building ubicomp systems. At the same time, we aimed to achieve light-weight, flexible, powerful, reactive user modeling that is accountable, transparent and scrutable.

Virtual reality (VR) exergames have the potential to be a fun way to get exercise. People have different preferences and responses when it comes to both exercising an playing games, meaning that there are potential benefits from creating a user model for exergaming. This could support various forms of personalization, such as game recommenders, and personalization within a game. We define a VR exergame user model, VRex, that represents a user's exertion as well a their goals and preferences for exercise and for games We illustrate the use of VRex to represent 1 users who played 4 games, based on data about their actual and perceived exertion and their satisfaction with each game. This demonstrates the diversity of the user models, in terms of the user model's components. This is the first work to explore the design of user models for virtual reality exergames and has the potential to serve as a foundation for game personalization, recommenders and open model interfaces.

Mid-air gestures offer a promising way to interact with large public displays. User representations are important to attract people to such displays, convey interactivity and provide meaningful gesture feedback. We evaluated two forms of user representation, an abstract skeleton and a silhouette, at a large public information display. Results from 56 days, with 190 sessions involving 483 detected people, indicate the silhouette attracted more passers-by to interact and, of these, more engaged in serious browsing interactions. By contrast, the skeleton representation had more playful interactions. Our work contributes to the understanding of the implications of these choices of user representation.

There is a steadily growing interest in the design of spaces in which multiple interactive surfaces are present and, in turn, in understanding their role in group activity. However, authentic activities in these multi-surface spaces can be complex. Groups commonly use digital and non-digital artefacts, tools and resources, in varied ways depending on their specific social and epistemic goals. Thus, designing for collaboration in such spaces can be very challenging. Importantly, there is still a lack of agreement on how to approach the analysis of groups' experiences in these heterogeneous spaces. This paper presents an actionable approach that aims to address the complexity of understanding multi-user multi-surface systems. We provide a structure for applying different analytical tools in terms of four closely related dimensions of user activity: the setting, the tasks, the people and the runtime co-configuration. The applicability of our approach is illustrated with six types of analysis of group activity in a multi-surface design studio.

With the arrival of 'big data; in education, the potential was recognised for learning analytics to track students' learning, to reveal patterns in their learning, or to identify at-risk students, in addition to guiding reform and supporting educators in improving teaching and learning processes [1]. Learning Analytics dashboards have been used at all levels, including institutional, regional and national level [2]. In classroom use, while learning visualisations are often based on counts of activity data or interaction patterns, there is increasing recognition that learning analytics relate to learning, and should therefore provide pedagogically useful information [3]. While increasing numbers of technology-enhanced learning applications are embracing the potential of learning analytics at the classroom level, often these are aimed at teachers. However, learners can also benefit from learning analytics data (e.g. [4][5]).

Single display interactive groupware interfaces have the potential to effectively support small group work in classrooms. Our work aimed to gain understanding needed to realize that potential. First, we wanted to study how learners use these large interactive displays, compared with a more traditional method within classrooms . Second, we wanted to fill gaps in the current understanding of the effectiveness of interactive tables versus walls . Third, we wanted to do this out of the laboratory setting, in authentic classrooms , with their associated constraints. We conducted an in-the-wild study, with 51 design students, working in 14 groups, learning the brainstorming technique. Each group practiced brainstorming in three classrooms: one with vertical displays (walls); another with multi-touch tabletops; and the third with pens and index cards. The published literature suggested that tabletops would be better than the other conditions for key factors of cooperative participation, mutual awareness, maintaining interest and affective measures. Contrary to this, we found that the horizontal and vertical displays both had similar levels of benefit over the conventional method. It was only for affective measures that tabletops were better than walls. All conditions were similar for our several measures of outcome quality. We discuss the implications of our findings for designing future classrooms.

This paper describes the design and evaluation of our Media Ribbon, a large public interactive display for browsing hierarchical information, with mid-air gestures. Browsing a hierarchical information space is a fundamental form of interaction. Designing learnable mid-air gestures is a current challenge for large display interaction. Our in-the-wild evaluation draws on 41 days of quantitative log data, with 4484 gestures detected, and qualitative data from 15 interviews, and associated video. We explored: whether our design enabled people to learn the gestures; how our tutorial and feedback mechanisms supported learning; and the effectiveness of support for browsing hierarchical information. Our contributions are: (1) design of large public display for browsing of hierarchical information; (2) with its gesture set; (3) insights into the ways people learn and use this interface in our context; and (4) guidelines for designing learnable mid-air gestures.

Designing, deploying and validating learning analytics tools for instructors or students is a challenge requiring techniques and methods from different disciplines, such as software engineering, human-computer interaction, educational design and psychology. Whilst each of these disciplines has consolidated design methodologies, there is a need for more specific methodological frameworks within the cross-disciplinary space defined by learning analytics. In particular there is no systematic workflow for producing learning analytics tools that are both technologically feasible and truly underpin the learning experience. In this paper, we present the LATUX workflow, a five-stage workflow to design, deploy and validate awareness tools in technology-enabled learning environments. LATUX is grounded on a well-established design process for creating, testing and re-designing user interfaces. We extend this process by integrating the pedagogical requirements to generate visual analytics to inform instructors' pedagogical decisions or intervention strategies. The workflow is illustrated with a case study in which collaborative activities were deployed in a real classroom.

My work aims to enable people to harness, control and manage their big personal data. This is challenging because people are generating vast, and growing, collections of personal data. That data is captured by a rich personal digital ecosystems of devices, some worn or carried, and others are fixed or embedded in the environment. Users explicitly store some data but systems also capture the user's digital footprints, ranging from simple clicks and touches, to images, audio and video. This personal data resides in a quite bewildering range of places, from personal devices to cloud stores, in multitudes of silos.

Recent research has proposed Neo-Piagetian theory as a useful way of describing the cognitive development of novice programmers. Neo-Piagetian theory may also be a useful way to classify materials used in learning and assessment. If Neo-Piagetian coding of learning resources is to be useful then it is important that practitioners can learn it and apply it reliably. We describe the design of an interactive web-based tutorial for Neo-Piagetian categorization of assessment tasks. We also report an evaluation of the tutorial's effectiveness, in which twenty computer science educators participated. The average classification accuracy of the participants on each of the three Neo-Piagetian stages were 85%, 71% and 78%. Participants also rated their agreement with the expert classifications, and indicated high agreement (91%, 83% and 91% across the three Neo-Piagetian stages). Self-rated confidence in applying Neo-Piagetian theory to classifying programming questions before and after the tutorial were 29% and 75% respectively. Our key contribution is the demonstration of the feasibility of the Neo-Piagetian approach to classifying assessment materials, by demonstrating that it is learnable and can be applied reliably by a group of educators. Our tutorial is freely available as a community resource.

An important feature for many applications is the ability to support personalised and context-aware information delivery. User positioning and the use of maps are essential for this purpose. The RoughMaps platform accommodates the use of symbolic maps, which are often not-to-scale, non-linear, highly abstract in nature, and which often contain only the most salient and most relevant features of a map based on the immediate needs of a given user. This work describes the design, implementation, and validation of the RoughMaps research platform, for managing and retrieving contextually relevant symbolic maps for indoor positioning. This is to our knowledge one of the first platforms that focuses on the use of symbolic maps to support indoor positioning in personalised and context-aware mobile applications.

A computer science student is required to progress from a novice programmer to a proficient developer through the programming fundamentals sequence of subjects. This paper deals with the capturing and representation of learning progression. The key contribution is a web-based interactive tutorial that enables computer science educators to practice applying the Bloom Taxonomy in classifying programming exam questions. The tutorial captures participant confidence and self-explanations for each Bloom [5] classification exercise. The results of an evaluation with ten participants were analyzed for consistency and accuracy in the application of Bloom. The confidence and self-explanation measures were used to identify problem areas in the application of Bloom to programming fundamentals. The tutorial and findings are valuable contributions to future ACM/IEEE CS curriculum revisions, which are expected to have a continued emphasis on Bloom [1].

Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, auser model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related toprivacy,invisibilityof personalization,errors in user models,wasted user models, and the broad issue of enabling people tocontroltheir user models and associated personalization. We offerscrutable user modelsas a foundation for tackling these problems. This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.

New forms of natural user interfaces, particularly tabletops, are now a possibility due to the staggering advances in sensing and display technology. There is an immediate need for corresponding progress on the foundational software for exploiting the potential of these new hardware products. This requires research into natural and effective gestural interaction, with careful consideration of the core facilities needed for effective and consistent use. At the same time, an exploration of real-world tabletop applications will provide a basis for studying and advancing the core functionality.

The reciprocal recommender is a class of recommender system that is important for several tasks where people are both the subjects and objects of the recommendation. Some examples are: job recommendation, mentor-mentee matching, and online dating. Despite the importance of this type of recommender, our work is the first to distinguish it and define its properties. We have implemented RECON, a reciprocal recommender for online dating, and have evaluated it on a large dataset from a major Australian dating website. We investigated the predictive power gained by taking account of reciprocity, finding that it is substantial, for example it improved the success rate of the top ten recommendations from 23% to 42% and also improved the recall at the same time. We also found reciprocity to help with the cold start problem obtaining a success rate of 26% for the top ten recommendations for new users. We discuss the implications of these results for broader uses of our approach for other reciprocal recommenders.

Reciprocal recommender is a class of recommender systems that is important for tasks where people are both the subject and the object of the recommendation; one such task is online dating. We have implemented RECON, a reciprocal recommender for online dating, and we have evaluated it on a major dating website. Results show an improved success rate for recommendations that consider reciprocity in comparison to recommendations that only consider the preferences of the users receiving the recommendations.

This paper explores the nature of interfaces to support people in accessing their files at tabletop displays embedded in the environment. To do this, we designed a study comparing people's interaction with two very different classes of file system access interface: Focus, explicitly designed for tabletops, and the familiar hierarchical Windows Explorer. In our within-subjects double-crossover study, participants collaborated on 4 planning tasks. Based on video, logs, questionnaires and interviews, we conclude that both classes of interface have a place. Notably, Focus contributed to improved collaboration and more efficient use of the workspace than with Explorer. Our results inform a set of recommendations for future interfaces enabling this important class of interaction -- supporting access to files for collaboration at tabletop devices embedded in an ubicomp environment.

The foundations of usability evaluation were being established in the early 1990s. In this context, “Usability – Context, Definition, Design and Evaluation” built upon Brian Shackel’s earlier influential work – work that helped define the notion of usability. In this paper, he established key dimensions of usability as well as approaches to integrating the testing of these dimensions, within the whole process of setting requirements. Essentially he argued for usability design as part of the system design process. This commentary describes the context of Professor Shackel’s paper and reviews the influential ideas that appear in much subsequent work.

Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.

In this paper, we describe ONCOR, our ontology- and evidence-based approach to reason about context. ONCOR tackles the critical problem of providing a flexible and pragmatic approach to building light-weight ontologies of places, devices and sensors in MyPlace, a ubiquitous computing application which provides personalised information about a building. Our contributions lie in constructing a general middle ontology for a building (MIBO) based on OpenCyc, combined with our own approach to evidence-based reasoning, (accretion and resolution). We describe the evaluation of ONCOR in terms of a comparison of its answers to core ontological questions about context with previous work.

We have recently begun to see hardware support for the tabletop user interface, offering a number of new ways for humans to interact with computers. Tabletops offer great potential for face-to-face social interaction; advances in touch technology and computer graphics provide natural ways to directly manipulate virtual objects, which we can display on the tabletop surface. Such an interface has the potential to benefit a wide range of the population and it is important that we design for usability and learnability with diverse groups of people.This paper describes the design of SharePic -- a multiuser, multi-touch, gestural, collaborative digital photograph sharing application for a tabletop -- and our evaluation with both young adult and elderly user groups. We describe the guidelines we have developed for the design of tabletop interfaces for a range of adult users, including elders, and the user interface we have built based on them. Novel aspects of the interface include a design strongly influenced by the metaphor of physical photographs placed on the table with interaction techniques designed to be easy to learn and easy to remember. In our evaluation, we gave users the final task of creating a digital postcard from a collage of photographs and performed a realistic think-aloud with pairs of novice participants learning together, from a tutorial script.

We describe a visualisation tool, VlUM, designed to support users in scrutinising models of their interests, preferences and knowledge. We also describe MECUREO, a tool for building lightweight ontologies from online dictionaries. It enables a user to see a model describing both their interests and additional analogically inferred interests. We report a small qualitative evaluation of this combined system. This indicates that users were able to use the system to explore models of two hypothetical users, making use of varying levels of ontological inference.

This paper describes a system which automatically constructs an extensive ontology of computer science. This can serve as a basis for making inferences about student models and other reasoning within a teaching system. The system enables a user to select a focus concept and then see the most closely related concepts.

Concept mapping is a valuable technique for education evaluation. This paper describes a system which supports teachers in creating concept mapping tasks which are intended to capture the student's understanding of the ontology of a small domain. A novel feature of our work is that the system verifies that the student intended the map elements that will be used to infer student understanding and misconceptions.

SITS (Scrutable Intelligent Teaching System) is designed to make use of existing learning items in flexible and effective learning interactions. The reuse of pre-existing resources is important since creating new learning resources is a time consuming task which requires a skilled author. The Internet also provides a large number of resources for reuse. A major hurdle for reuse is metadata, especially epistemological metadata since different teachers or courses may have different ontologies for a given domain. SITS takes a minimalist approach to metadata. It requires only that the author of a teaching environment should define document metadata specifying the concepts which each document teaches, requires, and uses.

Computer Science Education is gradually emerging as a valid research focus within the wider computer science community. This paper presents an account of a successful subject that offers senior undergraduates experience of research using Computer Science Education as the focus. The paper is not itself a research paper, but advocates a role for CS Ed as an undergraduate research focus and offers a case study of a successful implementation of such a programme.

Mentor programs are a highly effective way of assisting students with the transition from high school to university. This paper documents the introduction of the mentor program in our department. In this program, higher year students volunteered to act as mentors to incoming freshmen. Mentors fulfilled technical and social roles assisting freshmen with the transition to university and with their studies in computer science.We explain the details of mentor selection and their activities during the semester. We evaluate the outcomes of mentoring for students, mentors and the department.

Classification of email is an important everyday task for a large and growing number of users. This paper describes the i-ems (Intelligent-Electronic Mail Sorter) mail interface, which offers a view of the inbox based on predicted classifications of messages. The interface is designed to ensure user control over the prediction processes by supporting scrutiny of the system's certainty and details of the mechanisms used.

Educational theory suggests that student learning is enhanced when students pay attention to their own learning. In this paper, we describe a range of innovative techniques that we use to encourage students to reflect on the state of their knowledge, and on the process by which they acquire it. Examples include providing web-based arrangements for students to practice assessing material based on the criteria we use in marking, and allocating marks in assessment for reflective writing.

This paper describes a course that addresses two important issues: introducing interface design and programming at the same time as helping students develop life-long learning skills. Many computer science programmes could well be based on the assumption that the computing world is still oriented towards batch processing. Yet, interactive systems are central to information technology and the interface to a system is very important. Our courses introduces students to this area. At the same time, programming tools for building interfaces are an excellent example of very fast changing technology. Our course explicitly aims to help students learn to how to learn about such tools.The course is based on the approach called problem-based learning: the need to solve an authentic problem drives the definition of appropriate learning goals and strategies. An unusual feature of the course is the interweaving of the learning about programming tools and the classic skiIls of user interface design. We describe our course and its evaluation in a class of over 100 senior year undergraduate computer science students.

The paper describes the design and implementation of an intelligent tutoring system for the Dijkstra-Gries programming methodology as defined by Gries (1981) in "The Science of Programming". The first part of the paper identifies the requirements of intelligent tutoring systems in general and those of the methodology in particular. It shows the suitability of the Smalltalk environment for developing expandable intelligent systems and the compatibility of Smalltalk's object-oriented paradigm with the Gries methodology's goal/plan approach to programming. We then describe how these requirements are met: an overview of the system's support of the methodology and the modules that enable the system to respond intelligently. As an example, a reusable tutorial part is presented, first from a student's perspective, then from an author's perspective. Finally the results of an evaluation of the system drawn from actual student experience are presented.>