This paper introduces a tool for auto-generating criteria-oriented feedback in higher education. A model is trained based on 39 historical student submissions and teacher ratings. The tool combines natural language processing with large language model capacities to analyze contextual criteria, for which regression trees are trained. It aims to mimic teachers when providing feedback utilizing analytic rubrics. The evaluation results with 38 new submissions reveal that predictions are good, serving as a fruitful base to optimize the process when providing formative feedback.

Personalizing online courses has been the subject of exploration for decades. Central to this discourse is the contentious concept of learning styles, which has given rise to the meshing hypothesis. In this paper, that concept has been transferred from traditional learning styles to preferences for certain instructional methods. Especially gamified elements, competitions, and group work modes have been imitated to either be incorporated or excluded within an adaptive course besides conventional personalization features. A controlled experiment was conducted to evaluate the effect of the preference-based meshing hypothesis. The results revealed a statistically significant performance difference in the final, most challenging task (with$p=. 039$in the Welch test) for a specific setting. This study underscores the potential value of focusing on the meshing hypothesis, particularly concerning preferences for instructional methods, as a significant area for future research.

Computer-generated feedback can be created manifold. This paper compares two approaches for generating feedback: rule-based and prediction-based. Both approaches have several advantages and disadvantages, which are discussed in detail considering precision, recall, human effort for model creation, and explainability requirements.

Learning material is designed based on didactical concepts and methods. To create an item sequence, strategies are applied by rule-based descriptions. Merging them requires their combination, which is complex to apply. In this paper, strategy combination is applied using LSTMs. Samples are generated to train different LSTM models for specific strategies. Those models are then merged by averaging their weights. The approach gives teachers the controllability over the selection of an appropriate trade-off between different strategies, without the need to create complex rules by hand.

Online courses can be adapted to suit learner needs. Although it is known that learners are diverse, courses are often optimized using split tests to find an optimum that results in the best performance for the majority of participants. This is the best-practice approach, which is cost-efficient using well-defined statistical fundaments. However, learners are considered as one cohort, independently of subgroups, and their existence is seldom further analyzed. In this paper, we examine two versions of a 45min language learning online course, which cover the same learning content, but one version is enriched by simulations to create different settings of being observed, collaborating with a peer, or taking part in a competition. T-tests over all 157 users identify some tasks, which are optimum for the majority of learners. Nevertheless, such tasks must not be the best for everyone. If learners are split by preference levels, learner performances differ, but the results are in line with the literature, without statistically significance.

Many prediction tasks can be done based on users’ trace data. This paper explores divergent and convergent thinking as person-related attributes and predicts them based on features gathered in an online course. We use the logfile data of a short Moodle course, combined with an image test (IMT), the Alternate Uses Task (AUT), the Remote Associates Test (RAT), and creative self-efficacy (CSE). Our results show that originality and elaboration metrics can be predicted with an accuracy of ~.7 in cross-validation, whereby predicting fluency and RAT scores perform worst. CSE items can be predicted with an accuracy of ~.45. The best performing model is a Random Forest Tree, where the features were reduced using a Linear Discriminant Analysis in advance. The promising results can help to adjust online courses to the learners’ needs based on their creative performances.

Online courses have very high dropout rates worldwide. Learners are demotivated based on bad learning experiences. While some factors of online courses could be optimized for all learners, e.g. the quality, it is essential to note that a one-size-fits-all environment is not existing. Some learners are comfortable with certain methods while others may not. In the paper, we identified five learner preferences that can be used to adapt teaching methods in online courses. We provide a 10-item questionnaire, validate it based on exploratory factor analysis, and examine whether learners differ in preferences.

Online courses have very high dropout rates worldwide. Learners are demotivated based on bad learning experiences, lack of time, or motivation. While some factors of online courses could be optimized for all learners, e.g. the quality, it is essential to note that a one-size-fits-all solution is not sufficient. Learners have different preferences. They feel well with some methods while others may not. In this paper, we predict five learning preferences based on trace and performance data. The promising result shows that we can predict the values of our five preferences with acceptable accuracy up to.75, which can be used for further adaptions.

The combination of gaming and language learning is not new, but it has not been applied in open education adventure games. Although it is known that especially young people aim to play games, their combination with learning is mainly limited to simulations. This does not have a high impact on intrinsic motivation. This paper provides an engine to create adventure games with a virtual world based on images and a dialog-based storyline. Learners interact with virtual entities in plausible contexts using the foreign language only. If learners are unaware of a word’s meaning, cheating is possible, enhancing the user model. Based on that, learning material can be generated to practice unknown vocab by regularly interrupting the game. The generated micro-course needs to be finished before the game continues. We tested a game created using the engine and got wide acceptance.

In this paper, we use a Generative Adversarial Network (GAN) as a sequence generator for language learning online courses. Therefore, we cluster a very small dataset of manually created training samples to derive rules. Then, we train a GAN that can mimic rule-based sequences, where we use our derived rules to evaluate generated samples. We enhance our approach by a parameter that course creators can select deviations they want to have in new sequences without manual adjustments. The resulting sequences follow the core structure of the small sample set. Based on deviations of the generated new learning paths, new combinations of methods can be used that course creators did not previously have in mind. This opens up a new way to generate course sequences without the need to model many alternative learning paths for adaptions.

In language learning, getting corrective feedback for writing tasks is an essential didactical concept to improve learners' language skills. Although various tools for automatic correction do exist, open writing texts still need to be corrected manually by teachers to provide helpful feedback to learners. In this paper, we explore the usefulness of an auto-correction tool in the context of language learning. In the first step, we compare the corrections of 100 learner texts suggested by a correction tool with those done by human teachers and examine the differences. In a second step, we do a qualitative analysis, where we investigate the requirements that need to be tackled to make existing proofreading tools useful for language learning. The results reveal that the aim of enhancing texts by proofreading, in general, is quite different from the purpose of providing corrective feedback in language learning. Only one of four relevant errors (recall=.26) marked by human teachers is recorded correctly by the tool, whereas many expressions thought to be faulty by the tool are sometimes no errors at all (precision=.33). We provide and discuss the challenges that need to be addressed to adjust those tools for language learning.

Nowadays, online courses can be created in very efficient ways including interactive tasks using H5P. While it is easy to use this technology, the design of H5P has the disadvantage to be not useful for exams as answers can be found in the source code, which can be accessed without any effort. In this paper, we propose a technical concept to use interactive media in exams, created with H5P. Therefore, we provide insights into which parts need to be changed and how answers should be stored for grading.

The adaption of online courses according to the user knowledge is not new, but its application is limited. The major problem is the lack of missing open-source technology to personalize online courses with individual learning paths on a large scale. In this paper, we introduce our open-source framework that allows us to generate adaptive online courses in Moodle. We focus on language learning as an example implementation. Courses are generated based on a knowledge base and several XML templates that allow the generation of interactive tasks using H5P. The novelty of our approach is the application and combination of existing Moodle libraries to generate individual online courses, although they were not designed for that purpose. It is the first working example that supports individual learning paths on a large scale in Moodle. Besides, we provide technical details on how to overcome limiting problems by using Moodle as an LMS.

The following topics are dealt with: computer aided instruction; educational courses; teaching; further education; computer science education; natural language processing; Internet; educational institutions; educational administrative data processing; and human computer interaction.

Enabling students to recognize and evaluate the ubiquitous impact of computing technology on society is an internationally proclaimed goal of a K-12 computing education. To that end, students need to actually engage with their computing knowledge in concrete everyday situations. From the perspectives of learning transfer and variation theory, we conducted three iterations of a classroom intervention and qualitatively analyzed students’ learning processes. As a result, we propose a model of four so-called critical aspects of everyday computing technology in that context. We present various classroom situations and learning experiences in relation to those aspects, and discuss what seems to have enabled or prevented meaningful learning. In particular, we found that several students had difficulties in conceiving of computing technology as simultaneously economical and powerful, thus limiting its potential ubiquity. We discuss our findings in the context of contemporary theories of learning transfer and argue that they suggest specific issues that may seriously inhibit students to appropriately engage with their computing knowledge in the context of everyday technologies.

This paper proposes a crowd-programming approach for computational thinking education. An evaluation study was conducted with 61 high school students to investigate four hypotheses: 1) Learners using the computer-supported crowd-programming system require less time for problem-solving, 2) they need less attempts for correct solutions, 3) they tend not to request bottom-out hints for programming problems, and 4) they can solve more programming problems within 70 minutes than in other conditions.

In order to understand and evaluate computing technology in their environment, students first need to be able to identify it. This task becomes increasingly difficult, however, as computing systems become more and more ubiquitous and invisible. Based on the analysis of semi-structured focus interviews with 28 German pre-college students, we present a grounded theory of their conceptions and reasoning related to the identification of computing within technical devices. At its core is the finding that many students seemed to differentiate technical artifacts with respect to three conceived levels of capability. Many household appliances, for instance, were very well seen as electronic and programmed, but still as too limited in their capability to warrant the presence of a “real” computer or to be related to informatics. Given the increasing versatility, power, and associated risks of modern embedded systems as well as the advent of the internet of things, this issue should clearly be addressed. Based on our grounded theory, we propose some first ideas for how this might be done.

Course forums offer an interactive channel for learners to express opinions and feedback, which contain valuable emotions and topic information towards courses. In this paper, we propose an emotion oriented topic probabilistic model that can be used to calculate distributions of emotion-topic over words to discover what students are most concerned about. An experiment on real-life data indicates that students had a positive attitude for knowledge applications, a negative experience for the learning system, and expressed confusion about the final exam. We also visualize the temporal trends of emotions of the whole group and the groups with different levels of achievement. The proposed model has a potential in discovering students' emotions in their feedback, thus improving the online learning experience, and identifying at-risk students timely.

Abstract More than half of Germany’s functional illiterates are gainfully employed adults. This paper presents the eVideo approach that aims at people with poor basic education. The course “eVideo mobile - digital media in hospitality industry” is a game-based work-related eLearning course. It is based on an inclusive eLearning platform. Basic educational skills are addressed in workplace-related interactive videos and exercises on different competency levels. In a small pilot study users were observed while using the course and afterwards questioned in interviews.

Abstract The usage of gamification elements in learning contexts is getting more and more attention, as it can help increase learners’ motivation. Nevertheless accessibility and inclusion are seldom considered yet. In this paper an inclusive gamification concept for an eLearning course is presented. The main target group are people with poor basic education in hospitality industry. The developed gamification concept has been tested in a first small pilot study.

This paper reports on research conducted in a project named LISA which aims at supporting learners through learner-centered learning analytics using physiological sensor data as well as environmental sensors. We present the concept and a prototypical realization of a mobile sensor device used in LISA.

Limited access to patients is an increasing problem in medical education. In order to reduce patient shortage, we previously proposed a strategy for assigning patients to courses of future curricula based on routinely available patient and educational data. However, this algorithm did not consider challenges of an existing curriculum, thus greatly limiting its applicability in actual practise. This paper introduces a corresponding refinement of the algorithm together with its implementation of three algorithm variants approaches for resolving medical school courses that are affected by patient shortage. An evaluation of the new approach yielded that approximately two thirds of all respective course sessions could be resolved by applying several variants of the algorithm, each of which proved to have different strengths.

Learning programming can be a challenging task for students that not only requires them to acquire knowledge but also to make use of their knowledge in solving real-world problems. In this paper, we introduce an intelligent, adaptive and adaptable learning environment for Java programming called FIT Java Tutor. The learning environment integrates several pedagogical approaches in order to help learners learn programming considering individual needs. For testing purposes, we prepared a set of learning resources consisting of video tutorials, programming tasks, quizzes and multiple-choice tests, and deployed the learning system in an introductory programming class at Humboldt-Universitat zu Berlin. Based on experiences gained from this setup, we derived three research questions for investigation in future studies.

Collaborative learning is an educational strategy which is popularly used in project-based courses in schools and colleges. The diversity of group members is frequently considered to be a crucial criterion that can promote intensive intra-group interaction and successful learning outcomes. Yet, when the number of students is up to several hundreds, it is challenging for instructors to look for an optimal group formation considering maximal diversity of students in every group. To address this problem, this paper presents a discrete particle swarm optimization approach to compose heterogeneous learning groups. We carried out simulations based on optimizing the heterogeneity of gender and personality type. The experimental results show that the proposed approach is an effective and stable method that can support instructors to compose heterogeneous collaborative learning groups.

Building an Intelligent Tutoring System (ITS) from scratch usually requires technological skills, expertise about the domain and tasks, and pedagogical knowledge. We propose a middleware which facilitates the construction of intelligently supported learning systems independently from the underlying (formalized) domain knowledge using typical re-usable components of ITSs, exchangeable plug-ins, and machine learning techniques running in Matlab. We proved our concept using a web-based programming environment as an example of an user interface component interacting with our ITS middleware.

Traditional recommender systems are well established in scenarios in which "enough"items, users and ratings are available for the algorithms to operate on. However, automatic recommendations are also desirable in smaller online communities which only contain several hundred items and users. Collaborative filters, as one of the most successful technologies for recommender systems, do not perform well here. This paper argues that recommender systems can make use of contextual information and domain specific semantics in order to be able to generate recommendations also for these smaller usage scenarios. The new hybrid recommendation approach presented in the paper enhances traditional neighborhood-based collaborative filtering techniques through the use of new kinds of data and a combination of different recommendation methods (rule, demographic, and average based). While the algorithmic techniques presented in this paper are suitable (especially) for smaller online communities, they can also be applied to improve the quality of recommendations in larger communities. The approach was implemented and evaluated in a small regional bound parent education community. A multi-staged evaluation was conducted in order to determine the quality of recommendations: A cross-validation (recall), an expert questionnaire (recommendation quality) and a field study (user satisfaction). The results show that recommenders even for smaller communities are possible and can produce high quality recommendations.

This paper describes a study in which student-created diagrams about arguments in an ill-defined domain were manually graded by two independent human graders. Findings include that the graders overall agreed with each other on their grades, but their agreement was lower than one would expect in well-defined domains, and higher for solutions of extreme quality.

In this paper, we discuss design considerations and our plans to develop a generalized framework for intelligent support in educational argumentation systems. Our goal is to develop a framework that will support the development of argumentation learning systems across a variety of domains (e.g., the law, ethics).

The development of graphical argument models is an active and growing area of research in Artificial Intelligence and Law. The aim is to develop models which may be readily used by legal professionals and novices to produce and parse arguments. If this goal is to be realized it is important to develop models that human reasoners can manipulate and assess consistently. We report on an ongoing study of graph agreement in the context of the LARGO system.

While argumentation is highly important for humans in many different aspects of life, it is hard to teach large groups to argue. Classic face-to-face approaches, which have shown to be effective, are limited by personal and time issues. Thus there were attempts to support the learning of argumentation via collaboration tools and intelligent tutoring systems. A detailed review of about 50 argumentation systems indicated a lack of research on the architectural side as well as on the side of collaboration. This thesis will investigate how a generic, customizable software architecture and configurable flexible collaboration options can be used to support the learning of argumentation in different domains.

Supporting students in the acquisition of argumentation skills is an important goal of educational technology. However, there has not been much work done towards developing generic and reusable software architectures for collaborative argumentation that could reduce the development time for distributed argumentation learning systems. Based on a survey of more than 50 different argumentation systems, this paper presents a requirements analysis for a generic collaborative intelligent tutoring system for argumentation.

Diagrammatic models of argument are increasingly prominent in AI and Law. Unlike everyday language these models formalize many of the the components and relationships present in arguments and permit a more formal analysis of an arguments' structural weaknesses. Formalization, however, can raise problems of agreement. In order for argument diagramming to be widely accepted as a communications tool, individual authors and readers must be able to agree on the quality and meaning of a diagram as well as the role that key components play. This is especially problematic when arguers seek to map their diagrams to or from more conventional prose. In this paper we present results from a grader agreement study that we have conducted using LARGO diagrams. We then describe a detailed example of disagreement and highlight its implications for both our diagram model and modeling argument diagrams in general.

This paper presents a process model of arguing with hypotheticals and uses it to explain examples of oral arguments before the U.S. Supreme Court that are like those employed in Socratic law teaching. The process model has been partially implemented in the LARGO (Legal ARgument Graph Observer) intelligent tutoring system. The program supports students in diagramming oral argument examples; its feedback on students' diagrammatic reconstructions of the examples enforces the expectations of the process model. The paper presents empirical evidence that features of the argument diagrams made with LARGO are correlated with independent measures of argumentation ability. The examples and empirical results support the model's explanatory and diagnostic utility.

This paper describes an intelligent tutoring system, LARGO, that helps students learn skills of legal reasoning with hypotheticals by analyzing oral arguments before the US Supreme Court. The skills involve proposing a rule-like test for deciding a case, posing hypotheticals to challenge the rule, and responding by analogizing or distinguishing the hypotheticals and/or modifying the proposed test. Students diagram arguments in a special-purpose graphical language and receive feedback in the form of reflection questions.

Recently many car driving simulators have been developed and used for educational purposes. They can provide special training opportunities that are not available frequently in reality. However, currently existing simulators often rely on predefined situations and scenarios. Training students to deal with spontaneously occurring dangerous traffic situations is difficult when using hard wired "emergency" situations that occur in a predictable manner. In this paper we present a new approach to provide special learning opportunities. We adopt intelligent agent technologies to develop the "problem creator", a pedagogical agent who can deliberately cause dangerous situations. Introducing the problem creator into our collaborative 3D virtual car driving environment makes it unpredictable when, where, and what type of abnormal situations the students will be confronted with. These irregularly occurring challenges model the reality of car driving well.