Pande, Charuta
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A new approach for teaching programming: model-based agile programming (MBAD)
Designing courses for introductory programming courses with a heterogeneous audience (business and IT background as well) is a challenging task. In an internal project of the School of Business at the FHNW University of Applied Sciences and Arts Northwestern Switzerland (FHNW) a group of lecturers developed a concept entitled “Model-based agile development” (MBAD) which supports the learning of elementary programming concepts in an agile environment and builds the basis for advanced courses. MBAD will be used as a basic learning module for various Bachelor programs at the FHNW.
Hybrid conversational AI for intelligent tutoring systems
We present an approach to improve individual and self-regulated learning in group assignments. We focus on supporting individual reflection by providing feedback through a conversational system. Our approach leverages machine learning techniques to recognize concepts in student utterances and combines them with knowledge representation to infer the student’s understanding of an assignment’s cognitive requirements. The conversational agent conducts end-to-end conversations with the students and prompts them to reflect and improve their understanding of an assignment. The conversational agent not only triggers reflection but also encourages explanations for partial solutions.
A computational literature analysis of conversational AI research with a focus on the coaching domain
We conduct a computational analysis of the literature on Conversational AI. We identify the trend based on all publications until the year 2020. We then concentrate on the publications for the last five years between 2016 and 2020 to find out the top ten venues and top three journals where research on Conversational AI has been published. Further, using the Latent Dirichlet Allocation (LDA) topic modeling technique, we discover nine important topics discussed in Conversational AI literature and specifically two topics related to the area of coaching. Finally, we detect the key authors who have contributed significantly to Conversational AI research and area(s) related to coaching. We determine the key authors' areas of expertise and how the knowledge is distributed across different regions. Our findings show an increasing trend and thus, an interest in Conversational AI research, predominantly from the authors in Europe.
Visualization of patterns for hybrid learning and reasoning with human involvement
Practice track: a learning tracker using digital biomarkers for autistic preschoolers
Preschool children, when diagnosed with Autism Spectrum Disorder (ASD), often ex- perience a long and painful journey on their way to self-advocacy. Access to standard of care is poor, with long waiting times and the feeling of stigmatization in many social set- tings. Early interventions in ASD have been found to deliver promising results, but have a high cost for all stakeholders. Some recent studies have suggested that digital biomarkers (e.g., eye gaze), tracked using affordable wearable devices such as smartphones or tablets, could play a role in identifying children with special needs. In this paper, we discuss the possibility of supporting neurodiverse children with technologies based on digital biomark- ers which can help to a) monitor the performance of children diagnosed with ASD and b) predict those who would benefit most from early interventions. We describe an ongoing feasibility study that uses the “DREAM dataset”, stemming from a clinical study with 61 pre-school children diagnosed with ASD, to identify digital biomarkers informative for the child’s progression on tasks such as imitation of gestures. We describe our vision of a tool that will use these prediction models and that ASD pre-schoolers could use to train certain social skills at home. Our discussion includes the settings in which this usage could be embedded.
Towards an assistive and pattern learning-driven process modeling approach
The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.