Researchers use replication to confirm, strengthen, and advance Computing Education Research (CER). However, prior research shows that replication is infrequently used in CER, even though the community encourages its use. Previous research suggests that the CER community uses different terms to describe replication, which we aim to confirm in this Working Group (WG). We will conduct a Systematic Literature Review (SLR) across influential international CER venues to understand how researchers present and conduct replication studies, possibly identifying venues receptive to papers applying this research design. In addition, we will interview Computing Education researchers, conference leaders, and journal editors to understand their experiences and perceptions with replication. We expect to collect suggestions and recommendations on how CER can encourage more replication in future studies. Our work will highlight and confirm the terms the CER community uses to present replication studies, enabling researchers and educators to better identify these studies.

This poster presents the 150+ project, developed to support an overlooked population of informatics bachelor students: Near-completion students with at least 150 ECTS credits who re-enrol every semester for years and even decades, without graduating or dropping out.

Artificial Intelligence concepts are often introduced through mathematical formulas and programming assignments. While effective for advanced learners, these approaches can present barriers for learners with non-technical backgrounds.

This paper analyzes the most common problems limiting reproducibility of Information Retrieval research and provides researchers with insights and guidelines to improve the reproducibility of experiments and to allow the verification of obtained results. We conducted a study on 45 reproduction reports off 17 different papers, which have been published at renowned IR conferences. We analyzed the reports qualitatively and quantitatively and looked into the different insights from different groups. Occurring problems are classified into three problem families and 13 categories and afre then analyzed with respect to their influence on the reproduction process as well as on their frequency of appearance over time and per conference. Of these 17 different papers, 14 papers were reproducible to a certain degree without significant differences to the original results, but in many cases not the whole experiment was reproducible due to missing code, information or data. Also, we look at assumptions that were made when reproducing the different papers, as some experiment workflows were incomplete and information was missing. In addition, we propose recommendations to make machine learning research more reproducible and FAIR.

Despite numerous efforts to predict student dropout in university students, the exact variables influencing dropout remain elusive, as existing studies do not generalise well to other samples. The proposed PhD project uses meta science tools to critically examine sources of heterogeneity, identify relevant variables, and build a predictive model for dropout in informatics bachelor students at TU Wien.

This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.

We explore relationships between dynamics of emotion (arousal and valence) and topical stability in political discourse in two diachronic corpora of Austrian German. In doing so, we assess interactions among emotional and topical dynamics related to political parties as well as interactions between two different domains of discourse: debates in the parliament and journalistic media. Methodologically, we employ unsupervised techniques, time-series clustering and Granger-causal modeling to detect potential interactions. We find that emotional and topical dynamics in the media are only rarely a reflex of dynamics in parliamentary discourse.

The domain of German polarity dictionaries is heterogeneous with many small dictionaries created for different purposes and using different methods. This paper aims to map out the landscape of freely available German polarity dictionaries by clustering them to uncover similarities and shared features. We find that, although most dictionaries seem to agree in their assessment of a word’s sentiment, subsets of them form groups of interrelated dictionaries. These dependencies are in most cases an immediate reflex of how these dictionaries were designed and compiled. As a consequence, we argue that sentiment evaluation should be based on multiple and diverse sentiment resources in order to avoid error propagation and amplification of potential biases.