Teachers as designers of GBL scenarios: Fostering creativity in the educational settings

This paper presents a research started in 2010 with the aim of fostering the creativity of teachers through the design of Game-Based Learning scenarios. The research has been carried out involving teachers and trainers in the co-design and implementation of digital games as educational resources. Based on the results grained from the research, this paper highlights successful factors of GBL, as well as constraints and boundaries that the introduction of innovative teaching and learning practices faces within educational settings

Preparedness for Data-Driven Business Model Innovation:A Knowledge Framework for Incumbent Manufacturers

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.</p

Information for Impact: Liberating Nonprofit Sector Data

This paper explores the costs and benefits of four avenues for achieving open Form 990 data: a mandate for e-filing, an IRS initiative to turn Form 990 data into open data, a third-party platform that would create an open database for Form 990 data, and a priori electronic filing. Sections also discuss the life and usage of 990 data. With bibliographical references

Effects of corpus-based instruction on phraseology in learner English

This study analyses the effects of data-driven learning (DDL) on the phraseology used by 223 English students at an Italian university. The students studied the genre of opinion survey reports through paper-based and hands-on exploration of a reference corpus. They then wrote their own report and a learner corpus of these texts was compiled. A contrastive interlanguage analysis approach (Granger, 2002) was adopted to compare the phraseology of key items in the learner corpus with that found in the reference corpus. Comparison is also made with a learner corpus of reports produced by a previous cohort of students who had not used the reference corpus. Students who had done DDL tasks used a wider range of genre-appropriate phraseology and produced a lower number of stock phrases than those who had not. The study also finds evidence that students use more phrases encountered in paper-based concordancing tasks than in hands-on tasks.Unlike in previous DDL studies, observations of the learning of a specific text-type through DDL in the present study are based on the comparison with both a control learner corpus and an expert corpus.The study also considers the use of DDL with a large class size

A Business Analytics Approach to Strategic Management using Uncovering Corporate Challenges through Topic Modeling

Business analytics is a robust strategic management tool, and topic modeling is a technique that can be leveraged to derive insights from vast collections of unstructured data. Topic modeling is an automated method that identifies abstract concepts, or topics, present in various data sources, such as customer feedback, social media posts, and news articles. Through topic modeling, organizations can gain a better understanding of their customers, competitors, and market trends, which can be used to make informed strategic decisions, such as identifying new business opportunities, enhancing product or service offerings, and recognizing potential risks. Moreover, by integrating topic modeling with other business analytics approaches, such as predictive modeling, organizations can gain a more comprehensive perspective of their performance and make data-driven decisions. In essence, topic modeling is a valuable tool for strategic management that provides organizations with the insights they need to stay ahead of the competition and make informed decisions. To make effective strategic decisions, it is crucial to comprehend an organizations internal and external environments fully. The proposed approach utilizes text-mining techniques to augment traditional management tools, such as SWOT analysis or growth-share matrix. By examining narrative materials, such as financial disclosures, we apply topic modeling to identify critical challenges faced by an organization. We then quantify the language used in these materials in terms of risk and optimism, which provides a detailed understanding of a companys strengths and weaknesses and helps identify business units, activities, and processes that may be at risk. Additionally, this approach can be used to compare a company with its competitors or the broader market

D-WISE Tool Suite for the Sociology of Knowledge Approach to Discourse

Under the umbrella of the D-WISE project, manual and digital approaches to discourse analysis are combined to develop a prototypical working environment for digital qualitative discourse analysis. This new qualitative data analysis tool, called D-WISE Tool Suite, is built up in a process of close exchange by the two teams from humanities and informatics and focuses on developing central innovations regarding the availability of relevant Digital Humanities (DH) applications. Bridging the gap between structural patterns detected with digital methods and interpretative processes of human meaning making is at the core of the collaborative approach of anthropological studies and computer linguistics in the D-WISE project, which innovates both informatics technology of contextoriented embedding representations and hermeneutic methodologies for discourse analysis in the Sociology of Knowledge Approach to Discourse (SKAD). In this paper, the intertwining of the two paradigms Human-in-the-loop and AI-in-theloop will be presented by outlining the concept of Human Computer Interaction (HCI) in the D-WISE Tool Suite with its AI-empowered features and established modes of feedback-loops and the supported functions for facilitating SKAD

Managing Bias When Library Collections Become Data

Developments in AI research have dramatically changed what we can do with data and how we can learn from data. At the same time, implementations of AI amplify the prejudices in data often framed as ‘data bias’ and ‘algorithmic bias.’ Libraries, tasked with deciding what is worth keeping, are inherently discriminatory and yet remain trusted sources of information. As libraries begin to systematically approach their collections as data, will they be able to adopt and adapt the AI-driven tools to traditional practices? &nbsp; Drawing on the work of the AI initiative within Stanford Libraries, the Fantastic Futures conference on AI for libraries, archives, and museums, and recent scholarship on data bias and algorithmic bias, this article encourages libraries to engage critically with AI and help shape applications of the technology to reflect the ethos of libraries for the benefit of libraries themselves and the patrons they serve. A brief examination of two core concepts in machine learning, generalization and unstructured data, provides points of comparison to library practices in order to uncover the theoretical assumptions driving the different domains. The comparison also offers a point of entry for libraries to adopt machine learning methods on their own terms

Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh

The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lacks adequate representation in education, research, web applications etc. For instance, there is no option of translation to or from Amazigh / Berber on Google Translate, which hosts over 100 languages today. Consequently, we do not find specialized educational apps, L2 (2nd language learner) acquisition, automated language translation, and remote-access facilities enabled in Berber. Motivated by this background, we propose a supervised approach called DaToBS for Detection and Transcription of Berber Signs. The DaToBS approach entails the automatic recognition and transcription of Tifinagh characters from signs in photographs of natural environments. This is achieved by self-creating a corpus of 1862 pre-processed character images; curating the corpus with human-guided annotation; and feeding it into an OCR model via the deployment of CNN for deep learning based on computer vision models. We deploy computer vision modeling (rather than language models) because there are pictorial symbols in this alphabet, this deployment being a novel aspect of our work. The DaToBS experimentation and analyses yield over 92 percent accuracy in our research. To the best of our knowledge, ours is among the first few works in the automated transcription of Berber signs from roadside images with deep learning, yielding high accuracy. This can pave the way for developing pedagogical applications in the Berber language, thereby addressing an important goal of outreach to underrepresented communities via AI in education