The study of interaction data and learning experiences in education has the potential to allow the analysis of why students pass or fail a course or where a course can be improved. In the special case of programming courses for beginners, analyzing the behaviour of students while coding, is essential but not always possible. To avoid the need to install and setup a programming environment by the students, a web-based environment like the open source JupyterLab can be used. This paper presents Juxl, an extension of this online programming environment, allowing a modular, performant and secure way of logging user interactions and learning experiences. The logs are generated as xAPI statements and stored in a Learning Record Store to be further analyzed.

Virtual Reality offers many opportunities for use in learning and teaching. However, the creation of Virtual Reality learning opportunities need many resources. Systematic efforts are needed to design learning activities and prepare them for research to be able to show learning effectiveness in the future. This paper describes the planning, development and the evaluation method of Virtual Reality learning activities for Computer Graphics education. The focus is on the development process of the didactic concept, as well as the immersive Virtual Reality environment. Through the early integration of Learning Analytics components, the foundations for exploring insights into the learning processes are implemented. The methodical combination of software and didactic development offers the advantages of both approaches; the systematic collection of learning data enables early didactic monitoring and hypothesis generation for research.

As learning scenarios include more digital parts and often more than one online platform is used, Learning Analytics becomes more challenging to perform on these distributed data. In this paper, the Learning Analytics infrastructure EXCALIBUR LA is presented. It receives data from arbitrary platforms as xAPI statement, performs pro-active analyses by using various analytics engines and provides the analysis results via a REST API for each authorized platform. This concept allows to perform analyses adjusted to the needs of an institution and also to analyze data from multiple platforms combined.

This contribution presents a case study on the extension of traditional approaches to blended learning in theoretical computer science at university level by incooperating modern multitouch tabletop displays for collaborative learning. The evaluated serious game about regular expressions provides a collaborative learning experience for groups of students and allows precise control of difficulty. It includes gamification elements and allows extensive analytics of learner activities. The didactical approach is evaluated in a case study with randomized test and control groups consisting of students in the same semester who had no curricular contact with the topic prior to the experiment. The test group consisting of 34 students tackled regular expressions by playing the learning game with minimal intervention by an educator. The control group consisting of 59 students had a lecture about the same contents as the learning game. To measure the success of the game regarding learning outcome, three tests were taken, a pre-test prior to and a post-test after the instructional phase. To compare the sustainability of both methods, a third test was taken two weeks later. The results are discussed regarding the learning outcomes on different levels of Anderson and Krathwohl’s revised version of Bloom’s taxonomy of educational objectives.

First steps in programming often take place in a self-regulated learning process, online and without supervision or assistance of a teacher. When problems occur, novices depend on automated feedback from the programming learning environment or sample solutions, which do not necessarily fit the approach of the learners. Our goal is to identify and classify the problem-solving strategies of programming novices. In the long term, these insights might help with adaptive feedback fitting to the current trial of the individual player and to learn from strategies of successful learners. Using self-created visualization tools we found complex indicators for small-scale strategy patterns. These patterns, along with interaction data from the user interface, allow clustering the learners by their behaviour. Those insights should form a future basis for automatic recognition of strategies.

As anti-phishing games emerge as a scalable, motivational and effective approach to anti-phishing education for non-professional end-users, problems arise due to the missing relevance of a games' content and context. If a game presents examples unknown or without relevance to the user, the learning potential is limited as users have no point of reference. To provide more meaningful, relevant game content to users, we propose a personalization pipeline for data collection, content generation and delivery for anti-phishing learning games.

Following the trend of digitalization in university education, lectures and accompanying exercises and tutorials incorporate more and more digital components. These digital components spread from the usage of computers and tablets in tutorials to incorporating online learning management systems into the lectures. Despite e-Assessment being a valuable component in form of self-tests and formative assessment, the trend of digitalization has not yet been transferred on examinations. That is among other things caused by financial reasons, because maintaining a suitable IT-infrastructure for e-Assessment is expensive in terms of money as well as administrative effort. Bring Your Own Device is a potential solution to this issue, but also poses new challenges regarding the integrity of examinations. Therefore, we propose a framework for e-Assessment that tackles these challenges.

Learning analytics (LA) and Educational data mining (EDM) have emerged as promising technology-enhanced learning (TEL) research areas in recent years. Both areas deal with the development of methods that harness educational data sets to support the learning process. A key area of application for LA and EDM is learner modelling. Learner modelling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. As learning is increasingly happening in open and distributed environments beyond the classroom and access to information in these environments is mostly interest-driven, learner interests need to constitute an important learner feature to be modeled. In this paper, we focus on the interest dimension of a learner model and present Wiki-LDA as a novel method to effectively mine user’s interests in Twitter. We apply a mixed-method approach that combines Latent Dirichlet Allocation (LDA), text mining APIs, and wikipedia categories. Wiki-LDA has proven effective at the task of interest mining and classification on Twitter data, outperforming standard LDA.

In the last few years, there has been a growing interest in learning analytics (LA) in technology-enhanced learning (TEL). Generally, LA deals with the development of methods that harness educational data sets to support the learning process. Recently, the concept of open learning analytics (OLA) has received a great deal of attention from LA community, due to the growing demand for self-organized, networked, and lifelong learning opportunities. A key challenge in OLA is to follow a personalized and goal-oriented LA model that tailors the LA task to the needs and goals of multiple stakeholders. Current implementations of LA rely on a predefined set of questions and indicators. There is, however, a need to adopt a personalized LA approach that engages end users in the indicator definition process by supporting them in setting goals, posing questions, and self-defining the indicators that help them achieve their goals. In this paper, we address the challenge of personalized LA and present the conceptual, design, and implementation details of a rule-based indicator definition tool to support flexible definition and dynamic generation of indicators to meet the needs of different stakeholders with diverse goals and questions in the LA exercise.

Knowledge Management (KM) and Technology Enhanced Learning (TEL) became a very important issue in modern organizational professional learning and work process integration. Former learning and KM theories which characterize knowledge as a thing or process no longer fit today's digital world where the amount of required information is no more manageable and the half-time of knowledge in general is rapidly decreasing. Younger approaches such as the Learning as a Network (LaaN) theory describe knowledge as complex and emergent and put a heavier focus on knowledge networking. The LaaN theory further stresses the convergence of the learning and work processes in professional learning settings and views KM and TEL as two sides of the same coin. Driven by the LaaN theory, the Professional Reflective Mobile Personal Learning Environments (PRiME) project describes an integrated KM and TEL framework which connects learning and work processes. It enables the professional learner to harness implicit knowledge and offers knowledge networking at three different layers: the Personal Learning Environment (PLE), the Personal Knowledge Network (PKN) and the Network of Practice (NoP). Continuous knowledge networking results in constant evolution of knowledge leading to personal as well as organizational learning.

Massive Open Online Courses (MOOCs) have a remarkable ability to expand access to a large scale of participants worldwide, beyond the formality of the higher education systems. MOOCs support participants to be actively involved in collaborative learning and construct their own learning experience in a variety of domains. However, one of the biggest challenges facing MOOCs is how to assess the learners’ performance in a massive learning environment beyond traditional automated assessment methods. To address this challenge, peer assessment has been proposed as an effective assessment method in MOOCs. The problem is, however, how to ensure the quality of the peer assessment in terms of validity and reliability. Moreover, assessment in blended MOOCs (bMOOCs) introduces unique challenges regarding the best peer assessment model in a learning environment that brings together face-to-face interactions and online activities. This paper presents the details of a study conducted to investigate peer assessment in bMOOCs. The study results show that flexible rubrics have the potential to make the feedback process more accurate, credible, transparent, valid, and reliable, thus ensuring the quality of the peer assessment task.

A key area of application for Learning Analytics (LA) and Educational Data Mining (EDM) is lifelong learner modeling. The aim is that data gathered from different learning environments would be fed into a personal lifelong learner model that can be used to foster personalized learning experiences. As learning is increasingly happening in open and networked environments beyond the classroom and access to knowledge in these environments is mostly context-sensitive and interest-driven, learner’s contexts and interests need to constitute important features to be modeled. The context data of a learner as it is already represented by the Learning Context Data Model (LCDM) specification, describes the learner’s activities, her biological conditions, as well as the characteristics of the learning environment. Towards a lifelong learner model, a model consisting of context data can further be refined with an evolving set of interests. This paper describes an approach to extend the existing LCDM specification with interests, taking into account the importance of the interests as well as their evolution over time.

In North-Rhine Westphalia, the most populated state in Germany, Computer Science (CS) has been taught in secondary schools since the early 1970s. This article provides an overview of the past and current situation of CS education in North-Rhine Westphalia, including lessons learned through efforts to introduce and to maintain CS in secondary education. In particular, we focus on the differential school system and the educational landscape of CS education, the different facets of CS teacher education, and CS education research programs and directions that are directly connected with these aspects. In addition, this report offers a rationale for including CS education in general education, which includes the educational value of CS for students in today’s information and knowledge society. Through this article, we ultimately provide an overview of the significant elements that are crucial for the successful integration of CS as a compulsory subject within secondary schools.

Learning analytics (LA) deals with the development of methods that harness educational data sets to support the learning process. To achieve particular learner entered LA objectives such as intelligent feedback, adaptation, personalization, or recommendation, learner modeling is a crucial task. Learner modeling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. In this paper, we focus on learner modeling in academic networks. We present theoretical, design, implementation, and evaluation details of PALM, a service for personal academic learner modeling. The primary aim of PALM is to harness the distributed publication information to build an academic learner model.

Massive Open Online Courses (MOOCs) have gained a lot of attention in the last years as a new technology enhanced learning (TEL) approach in higher education. MOOCs provide more educational opportunities to a massive number of learners to attend free online courses around the globe. Discussions around MOOCs have been focusing on the potential, social, institutional, technological, relevance, and marketing issues and less on the quality design of MOOC environments. Several studies have reported a high drop-out rate in average of 95% of course participants and other pedagogical problems concerning assessment and feedback. Thus, the quality of MOOCs design is worth additional investigation. Although several studies identified a large set of criteria to the successful design of TEL systems in general, not all of them can be used in the MOOC context, due to some unique features of MOOCs. This study is a first step towards identifying specific criteria that need to be considered when designing and implementing MOOCs. The results of this empirical study are based on a large survey targeting learners as well as professors, both with MOOC experience. As a result, we identified and rated 74 indicators classified into our two main dimensions of pedagogical and technological criteria distributed over six categories. From these, the learning analytics and assessment categories were found to be the key features for effective MOOCs.

Learning analytics tools should be useful, i.e., they should be usable and provide the functionality for reaching the goals attributed to learning analytics. This paper seeks to unite learning analytics and action research. Based on this, we investigate how the multitude of questions that arise during technology-enhanced teaching and learning systematically can be mapped to sets of indicators. We examine, which questions are not yet supported and propose concepts of indicators that have a high potential of positively influencing teachers' didactical considerations. Our investigation shows that many questions of teachers cannot be answered with currently available research tools. Furthermore, few learning analytics studies report about measuring impact. We describe which effects learning analytics should have on teaching and discuss how this could be evaluated.

The Personal Learning Environment (PLE) driven approach to learning suggests a shift in emphasis from a teacher driven knowledge-push to a learner driven knowledge pull learning model. One concern with knowledge-pull approaches is knowledge overload. Thus, there is a crucial need for knowledge filters to help learners cope with the problem of knowledge overload. In this paper, we present the details of PLEM as a Web 2.0 driven service for personal learning management, which acts as a knowledge filter for learning. The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to help learners find quality knowledge nodes that can populate their PLEs.

Computer science faces the same problem worldwide: too few women in computing, low enrollment and high failure rates in university programs. There is growing consciousness which has led to various ideas and programs to deal with these problems. My talk will introduce a set of measures which we have implemented at RWTH Aachen University to tackle the problem on different levels. Therefore, I will present our successful program go4IT! of robotic courses for girls in 5th and 6th grade and some empirical results of how these influence their interest in STEM topics. Currently we are developing follow-up measures based on Android and GUI programming on smartphones. These measures cumulate into our newly founded Computer Science Junior Academy InfoSphere as out-of-school educational lab. There we offer a variety of learning modules which can last between two hours and a few days. Complete school classes can book InfoSphere modules or interested pupils of different ages can collaborate on self-organized projects. We have implemented various learning programs for multi-modal devices such as multi-touch tables and smartphones in order to introduce different topics of computer science. Our vision for these educational labs is the enhancement to different disciplines which bring together learners of all ages from K-12, students, researchers and professionals for lifelong learning and vocational training.

It is hard to accomplish effective communication and discussions in lectures with many participants. Most of in-class-eLearning systems, which try to facilitate interaction, only provide partial solutions. For instance clicker systems are good for votes and polls, but cannot foster qualified communication between students and lecturers. In this paper, we give reasons to use such a qualified tool and present a microblog system, MiRA, which offers various communication possibilities inside and outside the classroom. MiRA has been developed specifically to support mass lectures and has been tested at NameX University. We present major development steps and the results of surveys and system evaluations.

Assessment activities play a central role in university courses. Several types and methods of assessment can be combined to (a) motivate students to actively participate in exercises and (b) to assess learning outcomes. This article presents an approach to implement IT-supported standard processes for managing assessment activities in higher education. A model that is based on a directed acyclic graph (DAG) is used to define a reusable and shareable format of assessment criteria. A prototypical implementation shows how this approach can be realized.

Lack of proper English pronunciations is a major problem for immigrant population in developed countries like U.S. This poses various problems, including a barrier to entry into mainstream society. This paper presents a research study that explores the use of speech technologies merged with activity-based and arcade-based games to do pronunciation feedback for Hispanic children within the U.S. A 3-month long study with immigrant population in California was used to investigate and analyze the effectiveness of computer aided pronunciation feedback through games. In addition to quantitative findings that point to statistically significant gains in pronunciation quality, the paper also explores qualitative findings, interaction patterns and challenges faced by the researchers in dealing with this community. It also describes the issues involved in dealing with pronunciation as a competency.

Learning how to use a new software program can be a difficult and demanding task, especially for novices. There are several types of support for users exploring a software package. Animated demonstrations show how experts use an application, and training wheels interfaces offer a secure environment for exploration. To support different types of learners, external help should be adapted according to learner characteristics. The study presented in this article investigates effects of different support types in combination with the computer self-efficacy of learners. Young students (8th graders) were supported with text manuals, animated demonstrations, or animated demonstrations combined with a training wheels interface. In this context, they had to solve problems in physics and mathematics with a spreadsheet program. Results showed that animated demonstrations outperformed text manuals in many cases. Training wheels interfaces seemed to have disadvantages compared to unmodified user interfaces. In addition, motivational aspects have been investigated. Subjects with high computer self-efficacy scores were more motivated than their counterparts. Statistics (analysis of variance) revealed no interaction effects between the treatment and computer self-efficacy.

In this paper we describe an experiment, in which we examined older and younger adults when interacting with a simulated PDA (personal digital assistant). Independent variables were users' age (young vs. older) and device interface (hyperlink vs. no hyperlink). Dependent variables were the effectiveness and efficiency of menu navigation. To understand how user characteristics influence performance, spatial ability, verbal memory, computer expertise and technical self-confidence were determined. Technology experienced young and older adults (benchmark testing) took part. They had to solve four tasks either with hyperlink interface or without hyperlinks in the interface. The method to collect, to automatically analyze and to structure the data according to interaction sequences and presumed user intentions is a novel approach supported by the open source software tool Clever [12]. The tool is briefly described; more details can be found in [23]. Results revealed that hyperlink interfaces showed overall higher effectiveness. However, the impact of hyperlinks for efficiency was age-related. Younger adults strongly benefit from having hyperlinks. The contrary was the case for older adults, who showed higher menu disorientation when using hyperlinks.