Semi-Automatic Assessment of Modeling Exercises using Supervised Machine Learning

Motivation: Modeling is an essential skill in software engineering. With rising numbers of students, introductory courses with hundreds of students are becoming standard. Grading all students’ exercise solutions and providing individual feedback is time-consuming. Objectives: This paper describes a semi-automatic assessment approach based on supervised machine learning. It aims to increase the fairness and efficiency of grading and improve the provided feedback quality. Method: While manually assessing the first submitted models, the system learns which elements are correct or wrong and which feedback is appropriate. The system identifies similar model elements in subsequent assessments and suggests how to assess them based on scores and feedback of previous assessments. While reviewing new submissions, reviewers apply the suggestions or adjust them and manually assess the remaining model elements. Results: We empirically evaluated this approach in three modeling exercises in a large software engineering course, each with more than 800 participants, and compared the results with three manually assessed exercises. A quantitative analysis reveals an automatic feedback rate between 65 % and 80 %. Between 4.6 % and 9.6 % of the suggestions had to be manually adjusted. Discussion: Qualitative feedback indicates that semi-automatic assessment reduces the effort and improves consistency. Few participants noted that the proposed feedback sometimes does not fit the context of the submission and that the selection of feedback should be further improved

Increasing the Interactivity in Software Engineering MOOCs - A Case Study

MOOCs differ from traditional university courses: instructors do not know the learners who have a diverse background and cannot talk to them in person due to the worldwide distribution. This has a decisive influence on the interactivity of teaching and the learning success in online courses. While typical online exercises such as multiple choice quizzes are interactive, they only stimulate basic cognitive skills and do not reflect software engineering working practices such as programming or testing. However, the application of knowledge in practical and realistic exercises is especially important in software engineering education. In this paper, we present an approach to increase the interactivity in software engineering MOOCs. Our interactive learning approach focuses on a variety of practical and realistic exercises, such as analyzing, designing, modeling, programming, testing, and delivering software stimulating all cognitive skills. Semi-automatic feedback provides guidance and allows reflection on the learned theory. We applied this approach in the MOOC software engineering essentials SEECx on the edX platform. Since the beginning of the course, more than 15,000 learners from more than 160 countries have enrolled. We describe the design of the course and explain how its interactivity affects the learning success

2nd Workshop on Innovative Software Engineering Education

This workshop aims at presenting and discussing innovative teaching approaches in software engineering education, which are highly relevant for teaching at universities, colleges, and in online courses. The workshop focuses on three main topics: (1) project courses with industry, (2) active learning in large courses, and (3) digital teaching and online courses. © 2019 Gesellschaft fur Informatik (GI). All rights reserved

Integrating Competency-Based Education in Interactive Learning Systems

Artemis is an interactive learning system that organizes courses, hosts lecture content and interactive exercises, conducts exams, and creates automatic assessments with individual feedback. Research shows that students have unique capabilities, previous experiences, and expectations. However, the course content on current learning systems, including Artemis, is not tailored to a student's competencies. The main goal of this paper is to describe how to make Artemis capable of competency-based education and provide individual course content based on the unique characteristics of every student. We show how instructors can define relations between competencies to create a competency relation graph, how Artemis measures and visualizes the student's progress toward mastering a competency, and how the progress can generate a personalized learning path for students that recommends relevant learning resources. Finally, we present the results of a user study regarding the usability of the newly designed competency visualization and give an outlook on possible improvements and future visions.Comment: 4 pages, 2 figures. Best Practitioner Report Awar

ChatGPT-4 as a Tool for Reviewing Academic Books in Spanish

This study evaluates the potential of ChatGPT-4, an artificial intelligence language model developed by OpenAI, as an editing tool for Spanish literary and academic books. The need for efficient and accessible reviewing and editing processes in the publishing industry has driven the search for automated solutions. ChatGPT-4, being one of the most advanced language models, offers notable capabilities in text comprehension and generation. In this study, the features and capabilities of ChatGPT-4 are analyzed in terms of grammatical correction, stylistic coherence, and linguistic enrichment of texts in Spanish. Tests were conducted with 100 literary and academic texts, where the edits made by ChatGPT-4 were compared to those made by expert human reviewers and editors. The results show that while ChatGPT-4 is capable of making grammatical and orthographic corrections with high accuracy and in a very short time, it still faces challenges in areas such as context sensitivity, bibliometric analysis, deep contextual understanding, and interaction with visual content like graphs and tables. However, it is observed that collaboration between ChatGPT-4 and human reviewers and editors can be a promising strategy for improving efficiency without compromising quality. Furthermore, the authors consider that ChatGPT-4 represents a valuable tool in the editing process, but its use should be complementary to the work of human editors to ensure high-caliber editing in Spanish literary and academic books.Comment: Preprint. Paper accepted in the 18\textsuperscript{th} Latin American Conference on Learning Technologies (LACLO 2023), 14 page

An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder

Commonly, introductory programming courses in higher education institutions have hundreds of participating students eager to learn to program. The manual effort for reviewing the submitted source code and for providing feedback can no longer be managed. Manually reviewing the submitted homework can be subjective and unfair, particularly if many tutors are responsible for grading. Different autograders can help in this situation; however, there is a lack of knowledge about how autograders can impact students' overall perception of programming classes and teaching. This is relevant for course organizers and institutions to keep their programming courses attractive while coping with increasing students. This paper studies the answers to the standardized university evaluation questionnaires of multiple large-scale foundational computer science courses which recently introduced autograding. The differences before and after this intervention are analyzed. By incorporating additional observations, we hypothesize how the autograder might have contributed to the significant changes in the data, such as, improved interactions between tutors and students, improved overall course quality, improved learning success, increased time spent, and reduced difficulty. This qualitative study aims to provide hypotheses for future research to define and conduct quantitative surveys and data analysis. The autograder technology can be validated as a teaching method to improve student satisfaction with programming courses.Comment: Accepted full paper article on IEEE ITHET 202

Class I histone deacetylases 1, 2 and 3 are highly expressed in renal cell cancer

Background Enhanced activity of histone deacetylases (HDAC) is associated with more aggressive tumour behaviour and tumour progression in various solid tumours. The over-expression of these proteins and their known functions in malignant neoplasms has led to the development of HDAC inhibitors (HDI) as new anti-neoplastic drugs. However, little is known about HDAC expression in renal cell cancer. Methods We investigated the expression of HDAC 1, 2 and 3 in 106 renal cell carcinomas and corresponding normal renal tissue by immunohistochemistry on tissue micro arrays and correlated expression data with clinico-pathological parameters including patient survival. Results Almost 60% of renal cell carcinomas expressed the HDAC isoforms 1 and 2. In contrast, HDAC 3 was only detected in 13% of all renal tumours, with particular low expression rates in the clear cell subtype. HDAC 3 was significantly higher expressed in pT1/2 tumours in comparison to pT3/4 tumours. Expression of class I HDAC isoforms correlated with each other and with the proliferative activity of the tumours. We found no prognostic value of the expression of any of the HDAC isoforms in this tumour entity. Conclusion Class I HDAC isoforms 1 and 2 are highly expressed in renal cell cancer, while HDAC 3 shows low, histology dependent expression rates. These unexpected differences in the expression patterns suggests alternative regulatory mechanisms of class I HDACs in renal cell cancer and should be taken into account when trials with isoform selective HDI are being planned. Whether HDAC expression in renal cancers is predictive of responsiveness for HDI will have to be tested in further studies

Histone deacetylases 1, 2 and 3 are highly expressed in prostate cancer and HDAC2 expression is associated with shorter PSA relapse time after radical prostatectomy

High activity of histone deacetylases (HDACs) causes epigenetic alterations associated with malignant cell behaviour. Consequently, HDAC inhibitors have entered late-phase clinical trials as new antineoplastic drugs. However, little is known about expression and function of specific HDAC isoforms in human tumours including prostate cancer. We investigated the expression of class I HDACs in 192 prostate carcinomas by immunohistochemistry and correlated our findings to clinicopathological parameters including follow-up data. Class I HDAC isoforms were strongly expressed in the majority of the cases (HDAC1: 69.8%, HDAC2: 74%, HDAC3: 94.8%). High rates of HDAC1 and HDAC2 expression were significantly associated with tumour dedifferentiation. Strong expression of all HDACs was accompanied by enhanced tumour cell proliferation. In addition, HDAC2 was an independent prognostic marker in our prostate cancer cohort. In conclusion, we showed that the known effects of HDACs on differentiation and proliferation of cancer cells observed in vitro can also be confirmed in vivo. The class I HDAC isoforms 1, 2 and 3 are differentially expressed in prostate cancer, which might be important for upcoming studies on HDAC inhibitors in this tumour entity. Also, the highly significant prognostic value of HDAC2 clearly deserves further study