Hybrid classrooms are becoming increasingly prominent in higher education, yet significant misalignments persist between student expectations and current classroom designs. To address this, we conducted a multi-campus survey to evaluate student preferences for hybrid learning environments and dream classroom setups. The findings reveal critical gaps in ergonomic design, infrastructure, and technological integration, emphasizing the importance of foundational improvements in physical comfort and accessibility. This study proposes a three-phase framework for hybrid classroom evolution: (1) establishing ergonomic and infrastructural foundations, (2) integrating essential technologies like video conferencing tools, and (3) gradually adopting advanced solutions such as AI and VR for personalized learning. By aligning classroom design with pedagogical advancements, the proposed roadmap provides actionable strategies to create inclusive, adaptable, and student-centered learning environments. These findings underscore hybrid learning's potential as a transformative complement to traditional classrooms, bridging current gaps and fostering innovation in education.

The pandemic has forced lecturers and students in universities to suddenly move online with teaching and learning. Much work has gone into improving the online experience. We showed that learning outcomes improved at our university during this time according to self-evaluation by graduating students. Returning to on campus classes has caused some controversy. Two major thought-groups have emerged. While some have found the benefits of following lectures online using a better environment, others are grateful to return to their classroom to enjoy more freedom to interact with others. Strong feelings exist on both sides and students staying home are missed by those in the classroom. In order to understand these feelings, students have created a survey. 112 undergraduate students in Computer Science participated in the survey. We found that around half the students enjoyed staying online. Half of these report higher motivation and mental health when staying home. More research is needed but it is clear that the mix of on-site and online learning is here to stay.

The modern Computer Scientist has a set of desired skills and knowledge. Lately it has been recognized that there is a third dimension that ACM and IEEE call ”Disposition”. These are personal qualities (socio-emotional skills, behaviors, attitudes) associated with success in college and career and they should be listed as learning outcomes in modern curriculum design. In this paper we want to find out how far students gain the most important skills and dispositions in their studies. As a first step, local industry was asked which skills they think a Bachelor student needs in their daily work in order to determine the most important skills or dispositions. Subsequently, a survey was conducted that was based on these results in order to obtain students' viewpoints, including information on a specific course. They were asked to rank the skills from their point of view. In addition, they were specifically asked to comment on where they acquired the most important skills according to our industry ranking. Results show that the ranking differs from the one presented in the IEEE report both for industry and student responses. While industry and students agree mostly on the ranking, students report that the training of such skills is missing in industry but even more so at University.

Gamification in teaching is difficult and choosing the appropriate e-learning platform presents yet another major challenge. Game design thinking goes beyond simple gamification plug-ins to include motivational aspects like autonomy, mastery and purpose as well as personalized affinity spaces. This paper discusses some of the problems with existing platforms and focuses on the design of a light-weight central portal with appropriate workflow and progress visualization that joins remote personalized affinity spaces into the classroom. After discussing the system requirements the paper presents the implementation details of the portal platform. This work contributes an open-source implementation of a basic functional prototype.

Peer Reviews between students in higher education is the topic of this paper. By integrating this instruction method into university classroom activities, students train meta-skills and self-reflection, encouraged not only through giving constructive feedback to others but also by reflecting critically on received feedback for their own projects. Methodically, we analyzed over 500 peer reviews in a project-based two semester long Software Engineering class. First, the gamified set-up of the class design is described because the peer review constitutes an integral part thereof. The process of peer reviewing is then reported in detail, including a transcript of an interaction. Finally, we look at the content of peer reviews that are used to improve homework and estimate the number of improvements in the final project hand-in. It can be shown that the peer review contributes in a positive way to students' learning experience and the quality of their final hand-in.

Programming has become a crucial skill in today’s world. Technology and computers are basically part of anything now. President Obama once referred to the importance of children learning programming: “I do not want our young people to just be consumers, I want them to be producers of this technology and to understand it, to feel like they are controlling it, as opposed to it controlling them.” It is thus an increasing need for an individual to know how to write their own programs. With the increasing dependency on computation, one can anticipate such skills being a requirement for most jobs in the coming years. There is a lot of effort done in this area, however most of them focused on either software or hardware track. The aim of this work is to test if using a hybrid approach for teaching K-12 students programming by combining both software and hardware techniques is more effective than using a singleton approach.

Text-adventure games were one of the first types of games to be developed. They were referred to as multi-user dungeons or MUD. With improvement in graphics, MUDs were mostly replaced by video games but still retained selected fan groups. The idea behind this work utilizes the benefits of a text-adventure or a text-based game to help players improve their reading as well as their writing skills. The game is played by navigating through text-based commands and forces the player to read, understand the concept and then respond by writing correctly. Building on the affinity to chats, this interface can help students to both learn a language or practice correct orthography. In a first phase of building a successful interactive game, the goal is to adapt the experience to the player type by changing the interaction style and theme. The work presented here concentrates on identifying and catering to Achiever and Explorer according to Bartle's Taxonomy. The resulting game and prediction is evaluated with 35 students in order to fine-tune the classification system and 15 new students for testing the final system. Additionally, user feedback regarding language learning use and age-appropriateness of the task was collected.

Learning games now play a role in both formal and informal learning, including foundational skills such as literacy. While feedback is recognised as a key pedagogical dimension of these games, particularly in early learning, there has been no research on how commercial games available to schools and parents reify learning theory into feedback. Using a systematic content analysis, we examine how evidence-based feedback principles manifest in five widely-used learning games designed to foster young children's reading skills. Our findings highlight strengths in how games deliver feedback when players succeed. Many of the games, however, were inconsistent and not proactive when providing error feedback, often promoting trial and error strategies. Furthermore, there was a lack of support for learning the game mechanics and a preference for task-oriented rewards less deeply embedded in the gameplay. Our research provides a design and research agenda for the inclusion of feedback in early learning games.

This work is part of a series of publications on enabling education for children without access to schools, teachers and who have little parental guidance. New estimates by UNESCO reveal that 263 million children and youth are out of school. At the moment there exist no open source free online schools that do not expect a teacher to accompany the student journey. Without a teacher to help, a game environment can provide motivation, sequencing and content. The platform that we have been working on implements the framework for sorting educational games along the US common core standards. The current publication continues this work by adding the game interface that connects the external content with the child. A prototypical Role Playing Game (RPG) allows children to discover third party educational games leveled by grade and subject. The resulting game can be classified as a MOOC because it is an online course aimed at unlimited participation and open access via the web. The result of this work is the open source code of a game and an installed instance that can be used to connect third party games. The next step is to fill the underlying framework with educational games that hook into the various standards and evaluate the game with children.

The 6th International Workshop on Child Computer Interaction (WOCCI) was held in conjunction with ICMI 2017 in Glasgow, Scotland, on November 13, 2017. The workshop included ten papers spanning a range of topics relevant to child computer interaction, including speech therapy, reading tutoring, interaction with robots, storytelling using figurines, among others. In addition, an invited talk entitled ``Automatic Recognition of Children's Speech for Child-Computer Interaction'' was given by Martin Russell, Professor of Information Engineering at the University of Birmingham.

This paper presents conceptual ideas and a first prototype towards establishing a GamES (GAM)ified (E)lementary (S)chool MOOC for children who do not have access to schools. The project is being developed across several Bachelor student projects in collaboration between Cooperative State University, Karlsruhe and the German University of Cairo. A hybrid architecture is designed to support children on mobile devices with intermittent Internet access. Upon access, current learning status and new content are updated. The content consists of leveled games addressing skills defined by the US common core standards for K-5 in subjects Mathematics and English (that can be adapted to other languages). The children's MOOC combines ideas of adaptive user interface, off-line personalized learning, common core standards, and game-based learning. The child's user interface is gamified and after initial registration designed to be manageable for K-5 graders. A second interface addresses the community of game developers who are interested in donating games for certain core skills. In a next step, assessment and government certification of achievements will be tackled. The MOOC software is intended as open source to allow for community development.

Online education nowadays plays a very important role in enhancing the educational processes mostly for adults. Given this maturing technology and the number of children that lack access to safe education, mobile education for children is a logical next step and opens options that change their prospects. This paper is part of a larger project on mobile learning with games for children without access to schools. Games can motivate children to learn without the necessity of a teacher. The goal is to recommend learning games based on children’s preferences of past choices and ratings, which can supplement other recommender systems. The resulting implemented algorithm is designed as a plug-in to exisiting learning platforms that use games. Such a system was implemented and evaluated in a feasibility study on adults. We show that a prediction based on user’s choice and rating of games corresponds to a direct survey to determine the gamer types in 66% of the cases for 61 participants.

This paper studies the student view of functionality offered by a research-based design of a blended learning environment. The course in question is a Software Engineering course at the Cooperative State University of Baden-Wurttemberg in Karlsruhe. At our University, students alternate between study and work in a quarter-based system, completing their study in three years. Based on findings over the last years, the course is currently using an e-learning platform (coursesites) to enhance the classroom. For this paper, students were asked to evaluate the components of the platform and the functionalities offered. The results of the survey (77 students) shows which features of the platform are most used. Correlating the answers with theoretical model of types and platform offerings shows that functionality use can be a predictor of learner type. We show that the student body is highly fragmented between learner types with the main groups consisting of 38% avoidant, 27% collaborative/participant, and 10% competitive learner types. A single platform will not cover any mixed group of students and configurable views need to be considered in future.

This paper describes the implementation of a prototype for blended learning in a Software Engineering course at the Cooperative State University Baden-Wurttemberg in Karlsruhe. The University has certain particularities that distinguish it from other Universities because students alternate quarters between study and work. Thus, students receive a salary during their three years towards earning a Bachelor Degree and attendance is mandatory. In cohorts, around 30 students spend an average day with at least 5 hours of frontal lecture in the same classroom. Software Engineering takes up about 5 hours a week of in-class time in their second year of study and is the first course students have seen with a self-driven, blended learning format. The paper describes the set-up of the learning environment based on known research results of motivational factors. Based on an exploratory survey of 59 students, these motivation factors are compared to students’ motivations and their realizations in traditional and self-driven lecture format. Answers revealed that change presents a major challenge for most students and identifies the need for explicit habit building, change management and improved serving of students’ basic needs in a grade-based ecosystem.

Reading and writing are core competencies of any society. In Germany, international and national comparative studies such as PISA or IGLU have shown that around 25% of German school children do not reach the minimal competence level necessary to function effectively in society by the age of 15. Automized diagnosis and spelling tutoring of children can play an important role in raising their orthographic level of competence. One of several necessary steps in an automatic spelling tutoring system is the automatic correction of achieved text that was freely written by children and contains errors. Based on the common knowledge that children in the first years of school write as they speak, we propose a novel, context-sensitive spelling correction algorithm that uses phonetic similarities, in order to achieve this step. We evaluate our approach on a test set of texts written by children and show that it outperforms Hunspell, a well established isolated error correction program used in text processors.

The synthesis of child speech presents challenges both in the collection of data and in the building of a synthesizer from that data. We chose to build a statistical parametric synthesizer using the hidden Markov model (HMM)-based system HTS, as this technique has previously been shown to perform well for limited amounts of data, and for data collected under imperfect conditions. Six different configurations of the synthesizer were compared, using both speaker-dependent and speaker-adaptive modeling techniques, and using varying amounts of data. For comparison with HMM adaptation, techniques from voice conversion were used to transform existing synthesizers to the characteristics of the target speaker. Speaker-adaptive voices generally outperformed child speaker-dependent voices in the evaluation. HMM adaptation outperformed voice conversion style techniques when using the full target speaker corpus; with fewer adaptation data, however, no significant listener preference for either HMM adaptation or voice conversion methods was found.

This study compares two different methodologies for producing data-driven synthesis of child speech from existing systems that have been trained on the speech of adults. On one hand, an existing statistical parametric synthesiser is transformed using model adaptation techniques, informed by linguistic and prosodic knowledge, to the speaker characteristics of a child speaker. This is compared with the application of voice conversion techniques to convert the output of an existing waveform concatenation synthesiser with no explicit linguistic or prosodic knowledge. In a subjective evaluation of the similarity of synthetic speech to natural speech from the target speaker, the HMM-based systems evaluated are generally preferred, although this is at least in part due to the higher dimensional acoustic features supported by these techniques. Index Terms: child speech, statistical parametric speech synthesis, HMM-based speech synthesis, voice conversion, HTS, Average Voice Models, Festival

In this paper we examine various ways to derive confidence measures for a language identification system [3], using phone recognition followed by language models, and describe the application of an evaluation metric [1] for measuring the “goodness” of the different confidence measures. Experiments are conducted on the 1996 NIST Language Identification Evaluation corpus (derived from the Callfriend corpus of conversational telephone speech). The system is trained on the NIST 96 development data and evaluated on the NIST 96 evaluation data. Results indicate that we are able to predict the performance of a system and quantitatively evaluate how well the prediction holds on new data.

This paper studies the structure of foreign-accented read English speech. A system for accent identification is constructed by combining linguistic theory with statistical analysis. Results demonstrate that the linguistic theory is reflected in real speech data and its application improves accent identification. The work discussed here combines and applies previous research in language identification based on phonemic features [1] with the analysis of the structure and function of the English language [2]. Working with phonemically handlabelled data in three accented speaker groups of Australian English (Vietnamese, Lebanese, and native speakers), we show that accents of foreign speakers can be predicted and manifest themselves differently as a function of their position within the syllable. When applying this knowledge, English vs. Vietnamese accent identification improves from 86 % to 93 % (English vs. Lebanese improves from 78 % to 84%). The described algorithm is also applied to automatically aligned phonemes. 1.

A neural network based language identification system is described, which uses language independent phoneme clusters as speech units to recognize the language spoken by native speakers over the telephone. We extend our previous work comparing phoneme-cluster and phoneme based approaches to language identification [1]. By creating a new speech unit valid across all languages in a theoretically motivatedmanner, we circumvent problems that are associated with fine phonemic modelling such as high complexity [4], extensive training requirements [2], and the linguistically arbitrary reduction to subsets of phonemes [4]. A common set of speech units across languages allows us to automatically derive discriminating sequences of any length and theoretically estimate the language identification error. We demonstrateour implementedsystem for German vs. English on the OGI-TS database. 1. Introduction We view automatic language identification as the tagging of an incoming utterance with the corre...

This paper presents an analysis of the phonemic language identification system introduced previously (see Eurospeech, vol.2, p.1307, 1993), now extended to recognize German in addition to English and Japanese. In this system language identification is based on features derived from a superset of phonemes of all three languages. As we increase the number of languages, the need to reduce the feature space becomes apparent. Practical analysis of single-feature statistics in conjunction with linguistic knowledge leads to 90% reduction of the feature space with only a 5% loss in performance. Thus, the system discriminates between Japanese and English with 84.1% accuracy based on only 15 features compared to 84.6% based on the complete set of 318 phonemic features (or 83.6% using 333 broad-category features). Results indicate that a language identification system may be designed based on linguistic knowledge and then implemented with a neural network of appropriate complexity.>

We describe a system designed to recognize the language of an utterance spoken by any native speaker over the telephone. Our previous work based on language-specific phonemes [5] is extended to include sequences of all lengths of language-independent speech units. These units are derived by clustering phonemes across all languages in the system (Hindi, Spanish, English, German, Japanese, and Mandarin). Our language-identification results based on broad-phoneme occurrence statistics indicate 90% accurate distinction between English and Japanese, which is comparable to results obtained when using language-specific phonemes. By relaxing the precision of language-dependent phonemes into language-independent broad phonemes we thus retain language discriminative power. The degree to which the precision can be relaxed while retaining sequences of broad phonemes that can discriminate between languages is an indication of the accuracy with which the phoneme segmenter and recognizer have to reco...

A variety of approaches to language identification, based on (a) acoustic features, (b) broad-category segmentation, and (c) fine phonetic classification, are introduced. These approaches are evaluated in terms of their ability to distinguish between English and Japanese utterances spoken over a telephone channel. It is found that the best performance (86.3 % accurate classification of utterances with a mean length of 13.4 sec) is obtained when fine phonetic features are employed. In addition, the results show the importance of discriminatory training rather than likelihood estimation. 1. INTRODUCTION As developments in telecommunications and long-distance travel cause national borders to become increasingly transparent, the ability to identify which language is being spoken is growing in importance. The utility of tasks such as directory assistance or automatic translation is, for instance, improved substantially by the availability of a means of identifying which language is being s...