Information literacy and its interplay with AI

Information literacy (IL) – and similarly digital literacy – want to convey skills to handle information and data, its use and the creation of new information and services. It emphasizes to teach competencies that enable learners to adapt to new environments and thus foster life-long learning. Artificial intelligence systems (AI) enter all kinds of areas, specifically the educational sector on all levels. For example, learning analytics and learning supportive services are established. Learners might see the opportunities of those services that promise to foster individual learning and skill development. At the same time, they need to develop novel kinds of literacy to understand and to apply AI. Thus, IL teaching and literacy frameworks need to consider an adaptation to recent changes that come with AI. Our contribution wants to start a discussion within the IL expert field on how IL teaching needs to prepare learners for the new era of AI. We will discuss if IL teaching frameworks need to be adapted to foster AI literacy and moreover, how IL teaching concept can benefit from developments in AI. Based on ascoping review in AI in education, we will introduce current ideas of AI technology and applications and discuss them in relation to IL teaching schemes. Following up the dialog of our IL working group, we want to contribute to current discussions on AI in education and the potential influence it might have on IL teaching, and reversely. (DIPF/Orig.

Science 2.0 & open science in higher education

[no abstract available

Survey: Open Science in Higher Education

Based on a checklist that was developed during a workshop at OER Camp 2016 and presented as a Science 2.0 conference 2016 poster [1], we conducted an online survey among university teachers representing a sufficient variety of subjects. The survey was online from Feb 6th to March 3rd 2017. We got 360 responses, whereof 210 were completes, see raw data [2]. The poster is presented at Open Science Conference, 21.-22.3.2017, Berlin

Open Science nachhaltig gestalten

Es handelt sich um einen Bericht von der Open Science Conference 2018

Science 2.0 & Open Science in Higher Education

CHECKLIST – HOW CAN A LECTURER CONTRIBUTE TO OPEN LEARNING? Results from the OER Workshop, Feb. 2016, Berlin Link to open etherpad: 1st draft of checklist to bring open science into education [Checkliste für den Einsatz von Science 2.0 in der Lehre]: https://pad.okfn.org/p/science20_in_der_lehre_checklis

Survey: Open Science in Higher Education

Open Science in (Higher) Education – data of the February 2017 survey This data set contains: Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format. Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation. Readme file (txt) Survey structure The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS). Demographic questions Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification: Natural Sciences Arts and Humanities or Social Sciences Economics Law Medicine Computer Sciences, Engineering, Technics Other The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification: Professor Special education teacher Academic/scientific assistant or research fellow (research and teaching) Academic staff (teaching) Student assistant Other We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany. Remark on OER question Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them. Data collection The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey. The survey was online from Feb 6th to March 3rd 2017, e-mails were mainly sent at the beginning and around mid-term. Data clearance We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set. Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question No 24 (email address). References Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16. First results of the survey are presented in the poster: Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561 Contact: Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/. [1] https://www.limesurvey.org [2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”