Conversational Agents in Education – A Systematic Literature Review

Conversational Agents (CAs) are widely spread in a variety of domains, such as health and customer service. There is a recent trend of increasing publications and implementations of CAs in education. We conduct a systematic literature review to identify common methodologies, pedagogical CA roles, addressed target groups, the technologies and theories behind, as well as human-like design aspects. The initially found 3329 records were systematically reduced to 252 fully coded articles. Based on the analysis of the codings, we derive further research streams. Our results reveal a research gap for long-term studies on the use of CAs in education, and there is insufficient holistic design knowledge for pedagogical CAs. Moreover, target groups other than academic students are rarely considered. We condense our findings in a morphological box and conclude that pedagogical CAs have not yet reached their full potential of long-term practical application in education

Behavioral Task Modeling for Entity Recommendation

Our everyday tasks involve interactions with a wide range of information. The information that we manage is often associated with a task context. However, current computer systems do not organize information in this way, do not help the user find information in task context, but require explicit user actions such as searching and information seeking. We explore the use of task context to guide the delivery of information to the user proactively, that is, to have the right information easily available at the right time. In this thesis, we used two types of novel contextual information: 24/7 behavioral recordings and spoken conversations for task modeling. The task context is created by monitoring the user's information behavior from temporal, social, and topical aspects; that can be contextualized by several entities such as applications, documents, people, time, and various keywords determining the task. By tracking the association amongst the entities, we can infer the user's task context, predict future information access, and proactively retrieve relevant information for the task at hand. The approach is validated with a series of field studies, in which altogether 47 participants voluntarily installed a screen monitoring system on their laptops 24/7 to collect available digital activities, and their spoken conversations were recorded. Different aspects of the data were considered to train the models. In the evaluation, we treated information sourced from several applications, spoken conversations, and various aspects of the data as different kinds of influence on the prediction performance. The combined influences of multiple data sources and aspects were also considered in the models. Our findings revealed that task information could be found in a variety of applications and spoken conversations. In addition, we found that task context models that consider behavioral information captured from the computer screen and spoken conversations could yield a promising improvement in recommendation quality compared to the conventional modeling approach that considered only pre-determined interaction logs, such as query logs or Web browsing history. We also showed how a task context model could support the users' work performance, reducing their effort in searching by ranking and suggesting relevant information. Our results and findings have direct implications for information personalization and recommendation systems that leverage contextual information to predict and proactively present personalized information to the user to improve the interaction experience with the computer systems.Jokapäiväisiin tehtäviimme kuuluu vuorovaikutusta monenlaisten tietojen kanssa. Hallitsemamme tiedot liittyvät usein johonkin tehtäväkontekstiin. Nykyiset tietokonejärjestelmät eivät kuitenkaan järjestä tietoja tällä tavalla tai auta käyttäjää löytämään tietoja tehtäväkontekstista, vaan vaativat käyttäjältä eksplisiittisiä toimia, kuten tietojen hakua ja etsimistä. Tutkimme, kuinka tehtäväkontekstia voidaan käyttää ohjaamaan tietojen toimittamista käyttäjälle ennakoivasti, eli siten, että oikeat tiedot olisivat helposti saatavilla oikeaan aikaan. Tässä väitöskirjassa käytimme kahdenlaisia uusia kontekstuaalisia tietoja: 24/7-käyttäytymistallenteita ja tehtävän mallintamiseen liittyviä puhuttuja keskusteluja. Tehtäväkonteksti luodaan seuraamalla käyttäjän tietokäyttäytymistä ajallisista, sosiaalisista ja ajankohtaisista näkökulmista katsoen; sitä voidaan kuvata useilla entiteeteillä, kuten sovelluksilla, asiakirjoilla, henkilöillä, ajalla ja erilaisilla tehtävää määrittävillä avainsanoilla. Tarkastelemalla näiden entiteettien välisiä yhteyksiä voimme päätellä käyttäjän tehtäväkontekstin, ennustaa tulevaa tiedon käyttöä ja hakea ennakoivasti käsillä olevaan tehtävään liittyviä asiaankuuluvia tietoja. Tätä lähestymistapaa arvioitiin kenttätutkimuksilla, joissa yhteensä 47 osallistujaa asensi vapaaehtoisesti kannettaviin tietokoneisiinsa näytönvalvontajärjestelmän, jolla voitiin 24/7 kerätä heidän saatavilla oleva digitaalinen toimintansa, ja joissa tallennettiin myös heidän puhutut keskustelunsa. Mallien kouluttamisessa otettiin huomioon datan eri piirteet. Arvioinnissa käsittelimme useista sovelluksista, puhutuista keskusteluista ja datan eri piirteistä saatuja tietoja erilaisina vaikutuksina ennusteiden toimivuuteen. Malleissa otettiin huomioon myös useiden tietolähteiden ja näkökohtien yhteisvaikutukset. Havaintomme paljastivat, että tehtävätietoja löytyi useista sovelluksista ja puhutuista keskusteluista. Lisäksi havaitsimme, että tehtäväkontekstimallit, joissa otetaan huomioon tietokoneen näytöltä ja puhutuista keskusteluista saadut käyttäytymistiedot, voivat parantaa suositusten laatua verrattuna tavanomaiseen mallinnustapaan, jossa tarkastellaan vain ennalta määritettyjä vuorovaikutuslokeja, kuten kyselylokeja tai verkonselaushistoriaa. Osoitimme myös, miten tehtäväkontekstimalli pystyi tukemaan käyttäjien suoritusta ja vähentämään heidän hakuihin tarvitsemaansa työpanosta järjestämällä hakutuloksia ja ehdottamalla heille asiaankuuluvia tietoja. Tuloksillamme ja havainnoillamme on suoria vaikutuksia tietojen personointi- ja suositusjärjestelmiin, jotka hyödyntävät kontekstuaalista tietoa ennustaakseen ja esittääkseen ennakoivasti personoituja tietoja käyttäjälle ja näin parantaakseen vuorovaikutuskokemusta tietokonejärjestelmien kanssa

Enhancing Humanities Research Productivity in a Collaborative Data Sharing Environment

This project represents a multi-organizational, interdisciplinary effort to enhance collaborative research in cultural heritage fields by exploring user experience with Web-based technologies. The objective of this project is to document user needs around online systems for sharing primary data and documentation of cultural heritage collections. To this end, we will draw upon the experience and insights of representatives from different stakeholder groups in three broad arenas: academic researchers, heritage managers, and specialist communities. Investigations undertaken in this study will result in best-practice guidelines to guide humanities computing efforts on how to best meet the diverse user needs in future online data sharing systems. Using an iterative cycle of development, deployment, and evaluation, this project will enhance Open Context, a collaborative, open-access data sharing system already in use for archaeology and related disciplines

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Tsinghua Issue- Generative AI, Learning And New Literacies

Launched in November 2022, OpenAI\u27s ChatGPT garnered over 100 million users within two months, sparking a surge in research and concern over potential risks of extensive AI experiments. The article, originating from a conference presentation by Tsinghua University and NTHU, Taiwan, provides a nuanced overview of Generative AI. It explores the classifications, applications, governance challenges, societal implications, and development trajectory of Generative AI, emphasizing its transformative role in employment and education. The piece highlights ChatGPT\u27s significant impact and the strategic adaptations required in various sectors, including medical education, engineering, information management, and distance education. Furthermore, it explores the opportunities and challenges associated with incorporating ChatGPT in educational settings, emphasizing its support in facilitating personalized learning, developing 21st-century competencies, fostering self-directed learning, and enhancing information accessibility. It also illustrates the integration of ChatGPT and text-to-image models in high school language courses through the lens of new literacies. The text uniquely integrates three layers of discourse: introductions to Generative AI by experts, scholarly debates on its merits and drawbacks, and practical classroom applications, offering a reflective snapshot of the current and potential states of Generative AI applications while emphasizing the interconnected discussions across various layers of discourse

Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues

Groupware design : principles, prototypes, and systems

Computers are valuable tools for a wide range of work tasks. A substantial limitation on their value, however, is the predominant focus on enhancing the work of individuals. This fails to account for the issues of collaboration that affect almost all work. Research into computer supported cooperative work (CSCW) aims to eliminate this deficiency, but the promise of computer systems for group work has not been met. This thesis presents four design principles that promote the development of successful groupware. The principles identify the particular problems encountered by groupware, and provide guidelines and strategies to avoid, overcome, or minimise their impact. Derived from several sources, the major influence on the principles development is an investigation into the relationship between factors affecting groupware failure. They are stimulated by observations of groupware use, and by design insights arising from the development of two groupware applications and their prototypes: Mona and TELEFREEK. Mona provides conversation-based email management. Several groupware applications allow similar functionality, but the design principles result in Mona using different mechanisms to achieve its user-support. TELEFREEK provides a platform for accessing computer-supported communication and collaboration facilities. It attends to the problems of initiating interaction, and supports an adaptable and extendible set of "social awareness" assistants. TELEFREEK offers a broader range of facilities than other groupware, and avoids the use of prohibitively high-bandwidth communication networks. TELEFREEK demonstrates that much can be achieved through current and widely accessible technology. Together, Mona and TELEFREEK forcefully demonstrate the use of the design principles, and substantiate the claim of their utility

Sensitivity analysis in a scoping review on police accountability : assessing the feasibility of reporting criteria in mixed studies reviews

In this paper, we report on the findings of a sensitivity analysis that was carried out within a previously conducted scoping review, hoping to contribute to the ongoing debate about how to assess the quality of research in mixed methods reviews. Previous sensitivity analyses mainly concluded that the exclusion of inadequately reported or lower quality studies did not have a significant effect on the results of the synthesis. In this study, we conducted a sensitivity analysis on the basis of reporting criteria with the aims of analysing its impact on the synthesis results and assessing its feasibility. Contrary to some previous studies, our analysis showed that the exclusion of inadequately reported studies had an impact on the results of the thematic synthesis. Initially, we also sought to propose a refinement of reporting criteria based on the literature and our own experiences. In this way, we aimed to facilitate the assessment of reporting criteria and enhance its consistency. However, based on the results of our sensitivity analysis, we opted not to make such a refinement since many publications included in this analysis did not sufficiently report on the methodology. As such, a refinement would not be useful considering that researchers would be unable to assess these (sub-)criteria