Time: Wednesday, 28/Aug/2024: 9:30 - 11:00 Session Chair: Sandra Langer |
Location: Room LRC 017 in Library (Learning Resource Center "Stelios Ioannou" [LRC]) [Ground Floor] Cap: 48 |
Paper
Digital Generations, Children’s Academic Performance and Perceived Academic Ability
1University College Dublin, Ireland; 2University College London
Presenting Author:The rapid digitalization of society over the past decades has fundamentally changed how children and adolescents socialize, study, and play. Subsequently, children and adolescents’ use of digital technologies has increased rapidly, facilitated by the ever-evolving mobile accessibility and computing power of new digital technologies. Further, the current cohorts of children also experienced key developmental and socialization stages during the COVID-19 pandemic which led children and adolescents, by circumstance and necessity, to engage in higher levels of digital engagement. Such marked and rapid increases in both access and use of digital technologies, as well as the COVID-19 pandemic, has caused growing concerns in parents, researchers, educators, and clinicians alike as to what effects such technologies may have on children’s development and educational outcomes.
Overall, the current literature on the effects of digital use and child and adolescent educational outcomes is mixed. Some studies indicate that adolescent digital use, particularly texting, hampers children’s literacy outcomes (Kemp and Bushnell, 2011) and that early mobile phone ownership negatively impacts academic development (Dempsey et al., 2019). However, other studies found positive effects (Plester et al., 2008) or no associations (Verheijen, 2013). Some literature has examined the potential negative impacts of adolescent digital use on academic performance via cognitive functioning, including cognitive overload or multitasking (May and Elder, 2018), distraction and diminished attentional abilities (Ward et al., 2017), and memory and learning patterns (Loh and Kanai, 2016). Additionally, Lissak (2018) identified that the effects of digital use on academic performance may be indirectly channeled through reductions in sleep duration and quality, leading to problems of time displacement or sleep disruption.
As a whole, results on associations between digital use and academic and educational outcomes remains unclear, and further research on these associations with current cohorts of children remain essential to understand how today’s digital environments are affecting how children learn and develop. Further, while there have been a multitude of studies that have investigated the effects of the COVID-19 pandemic on children’s educational outcomes (Coles et al, 2023), few studies have examined how effects of digital use on child outcomes differ from other cohorts of children who did not experience the pandemic during the critical stage of childhood and early adolescence.
This study crucially aims to address some of the above gaps in knowledge. This study utilizes high-quality multi-cohort data to examine 1) how children at different stages of development are using digital technologies and 2) how these children’s digital use is associated with perceived academic ability (from both children and teachers). To do so, we utilize the most recently collected wave from the Children’s School Lives (CSL) study collected in April 2023, when the participating cohorts of children were age 8 and age 12/13. Preliminary analyses include descriptive statistics and OLS regression modelling, however, further analyses will incorporate more diverse regression modelling, longitudinal models as well as standardized testing data (not yet available) to compare perceptions vs realities of chidlren's academic ability.
Methodology, Methods, Research Instruments or Sources Used
This study utilizes data from the Children’s School Lives (CSL) study, a multi-cohort, longitudinal study from Ireland which aims to provide a rich and detailed understanding of children’s learning, wellbeing, and engagement. CSL follows two age cohorts: Cohort B, who were born in approx. 2010 who started 2nd class in 2018; and Cohort A, born in approx. 2015 who transitioned from pre-school into Junior Infants in 2019. Data collection began in April 2019, with both cohorts sampled every year through Spring 2023.
For the current analyses we utilize the most recent wave of data collection (Wave 5), in which the study children are approximately age 8 (Cohort A, N = 1,598) and age 12/13 (Cohort B, N = 1,911). Multiple imputation was applied on variables with high levels of missingness. This study first descriptively examines differences in digital technologies and digital screen-time between the two cohorts We then perform a number of OLS linear regression models to investigate associations of digital screen-time on a) children’s perceived academic ability and b) teacher’s perceived academic performance. Three OLS models were examined for each cohort and outcome variable: a univariate model, a model that includes sociodemographic controls (child gender, single parenthood, parental education, and household income) and a final model that include sociodemographic variables and previous perceived academic ability to preliminarily address issues of bidirectionality.
To measure digital screen-time, children were asked how much time they spend on screen-based activities on an average weekday and weekend day (Responses: None, 30 minutes, 1 hour, 2 hours, and 3+ hours). Further, children were asked what digital technologies they either own themselves or share regularly (smartphone, tablet, smartwatch, computer, games console). To examine children’s perceived academic ability, children were asked “Compared to other children in your class, how well do you think you do in [reading/maths]?”, with responses of ‘Struggling a lot’, ‘Struggling a little bit’, ‘Same as everyone else’, ‘A little bit better’, and ‘A lot better’. Teachers were also asked to assess the study children’s academic ability “typical ability compared to their peers?”, with responses of ‘Lower’, ‘Average’, or ‘Higher’. Although these outcome variables can be considered categorical or ordinal, for these preliminary analyses we utilize them as continuous variables where lower scores indicate lower perceived ability and higher scores indicate higher perceived ability. This was done to examine preliminary associations and trends, and further analyses will utilize and compare multinomial and ordinal logistic regression modelling.
Conclusions, Expected Outcomes or Findings
Both cohorts exhibit high engagement (approx. 70%) with tablets and consoles, while the older Cohort B uses/owns smartphones and computers at higher levels than Cohort A. In terms of screen-time, we observe that the older cohorts has overall higher rates of screen-time than the younger cohort, with nearly half of Cohort B spending over 3 hours on average per weekday on digital devices, compared to 28% of children from Cohort A. However, this is a drastic increase compared to data from previous research which found only 1-2% of Irish 9-year-olds (born in 1998 and 2008) to use digital technologies for 3+ hours per day (Bohnert & Gracia, 2021), this indicates that current generations of children and adolescents, particularly those who have experienced the COVID-19 pandemic, might be participating in much higher levels of screen-time than even very recent previous cohorts.
From the OLS models we first observe that 3+ hours weekday screen-time is significantly associated with lowered child perceptions of academic ability in Cohort B, in both reading (B = -0.244, p < 0.001) and math (B = -0.178, p < 0.01). We further observe a significant association of 3+ hours screen-time with reduced teacher perception of reading ability (B = -0.109, p < 0.05). However, we observe no significant associations of digital screen-time with perceptions of academic ability in Cohort A. The findings from Cohort B is in line with some previous research in Ireland which found negative associations between digital engagement and children’s academic development (Dempsey et al., 2019). Further, the differing associations between cohorts might indicate that effects of digital use on outcomes are somewhat delayed i.e. that significant negative effect might emerge later in childhood and adolescence (Kardefelt-Winther, 2017).
Overall, our preliminary results reveal key similarities and differences in the digital effects among current cohorts of Irish children.
References
Bohnert, M., & Gracia, P. (2021). Emerging digital generations? Impacts of child digital use on mental and socioemotional well-being across two cohorts in Ireland, 2007–2018. Child Indicators Research, 14, 629-659.
Bohnert, M., & Gracia, P. (2023). Digital use and socioeconomic inequalities in adolescent well‐being: Longitudinal evidence on socioemotional and educational outcomes. Journal of Adolescence.
Coles, L., Johnstone, M., Pattinson, C., Thorpe, K., Van Halen, O., Zheng, Z., ... & Staton, S. (2023). Identifying factors for poorer educational outcomes that may be exacerbated by COVID‐19: A systematic review focussing on at‐risk school children and adolescents. Australian Journal of Social Issues, 58(1), 13-40.
Dempsey, S., Lyons, S., & McCoy, S. (2019). Later is better: Mobile phone ownership and child academic development, evidence from a longitudinal study. Economics of Innovation and New Technology, 28, 798–815.
Kardefelt-Winther D (2017) How Does the Time Children Spend Using Digital Technology Impact Their Mental Well-Being, Social Relationships and Physical Activity? An Evidence-Focused Literature Review. Innocenti Discussion Paper 2017-02. Florence, Italy: Unicef Office Of Research-Innocenti.
Kemp N, and Bushnell C (2011) Children's text messaging: Abbreviations, input methods and links with literacy. Journal of Computer Assisted Learning 27(1): 18-27.
Lissak G (2018) Adverse physiological and psychological effects of screen time on children and adolescents: Literature review and case study. Environmental research 164: 149-157.
Loh KK, and Kanai R (2016) How has the Internet reshaped human cognition?. The Neuroscientist 22(5): 506-520.
May KE, and Elder AD (2018) Efficient, helpful, or distracting? A literature review of media multitasking in relation to academic performance. International Journal of Educational Technology in Higher Education 15(1): 1-17.
Plester B, Wood C, and Bell V (2008) Txt msg n school literacy: does texting and knowledge of text abbreviations adversely affect children's literacy attainment?. Literacy 42(3): 137-144.
Verheijen L (2013) The effects of text messaging and instant messaging on literacy. English studies 94(5): 582-602.
Paper
Exploring Teachers’ Media Literacy in Schools in Kazakhstan
Kazakh National Women’s Teacher Training University, Kazakhstan
Presenting Author:This study focuses on exploring teachers' media literacy (hereinafter, ML), including their competencies and practices of ML in the classrooms in Kazakhstan. In the 21st century children increasingly use digital tools and are exposed to different unfiltered media messages daily, wherein they have access to the Internet at home and communicate media messages regularly (Murray, 2021; OECD, 2020). The major concern is that a substantial number of children access media platforms in breach of age limitations and many of them actively use social media (Setyarini et al., 2023; Hill, 2022). This, in turn, requires teachers to develop ML competencies, so as to support their students’ ML who are largely susceptible to media influence (Reimers, 2009; Bystray et al., 2023). Studies also indicate that teachers’ ML competencies, socialisation and intercultural interaction are key to integrating ML into curriculum (Korona, 2020; Skantz-Åberg et al., 2022; Villacrez-Cuadros et al., 2023). Although the support for the development of teachers’ ML competencies has grown in the recent decade, few educators seem to use it in curricula development and lesson planning.
Teachers are identified as the most significant factor in enhancing students’ learning outcomes (Ingvarson et al., 2005). Teachers’ understanding of ML has a significant effect on the effectiveness of their teaching (Simons et al., 2017; Rohs et al., 2019; Saptono, 2022). Therefore, ML merits a place in teacher education, as it encourages an understanding of culture, connects educators, institutions, and society (Schwarz, 2001). In a similar vein, the studies highlight the importance of integrating language and ML into teacher education to facilitate socialisation and intercultural communication (Felini, 2014; Meehan et al., 2015; Schwarz, 2001).
Teachers’ ML competencies
The definition of media literacy as social phenomena focuses on technical, cognitive competencies and sociocultural pragmatics (Yeh & Swinehart, 2020). The technical competencies include functional skills as access, create, navigate, order, and distribute social media content (Daneels & Vanwynsberghe, 2017). Cognitive competencies refer to understanding, assessing, and critically analysing social media content for credibility and application (Daneels & Vanwynsberghe, 2017, Christ & Abreu, 2020). Socialcultural pragmatics provides awareness about social and cultural norms of behaviour, values, beliefs, language usage and discourses in media contexts (Yeh & Swinehart, 2020). Tandoc et al. (2021) claim that there could be four types of competencies such as technical, social, privacy related and informational in which social media literacy functions. Lately three themes were identified as teachers' perspectives of media literacy: assessing the validity of media messages, interacting with media, and safety issues (Von Gillern et al., 2024).
Teacher practices of ML
Inquiry is a strategy for implicitly teaching media literacy concepts that enables learners to “construct” new knowledge for themselves by adjusting new data with their prior knowledge (Brunner & Tally, 1999). This constructivist approach is complemented by media decoding, which means analysing and evaluating the messages conveyed by various forms of media (Scheibe & Rogow, 2011). Evidence-based practices are those which ensure high rates of proficiency and have a record of achievement that is valid and true (Gambrell et al., 2011). Critical inquiry is the core of constructivist media analysis, which means the ability to analyse media by asking key media literacy questions (Mason, 2016). Using the combination of inquiry and reflection is used extensively and is considered the basic way of integrating media literacy into any curriculum, constructivist media decoding strategy suggests the engaging acquisition of media literacy competencies (Scheibe & Rogow, 2011).
The aim of this study is to explore teachers’ media literacy in secondary schools in Kazakhstan. The study was structured around the following research questions:
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What is the state of teachers' media literacy competencies?
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How do teachers practise media literacy in their classrooms?
Methodology, Methods, Research Instruments or Sources Used
This paper presents a small-scale pragmatically-guided study employing a mixed-method research approach. The integration of both quantitative and qualitative data collection methods enabled me to ensure validity of the findings and understand the complex issues in social research (Creswell, 2014). Data collection tools included: (1) a small-scale questionnaire, (2) in-depth and semi-structured interviews, and (3) observations. The research participants represent teachers from public secondary schools located both in urban and rural areas of Kazakhstan. The purposeful sampling was used to carefully select teachers, who could offer relevant-to-the-topic information. As a result, qualitative data consisted of interviews and lesson observations of 9 teachers from 3 secondary schools in Kazakhstan. Also, 112 teachers filled in a small-case questionnaire, which assessed teachers’ ML competencies.
Data analysis
Qualitative data were analysed through thematic analysis and abductive coding (Pope, 2000). Thematic analysis was conducted in six phases: (1) becoming familiar with data; (2) generating codes; (3) looking for themes; (4) reviewing themes; (5) defining and naming themes, and (6) creating a report (Braun & Clarke, 2006). All interviews were transcribed, coded and grouped into the themes employing both inductive and deductive approaches (Miles, Huberman & Saldana, 2014). Quantitative research data were analysed descriptively and referentially, whereby multiple linear regression, assumptions, variables, and validity were tested. Items were developed based on the previous valid instrument of Simons et al. (2017), which was designed to measure teachers’ ML competencies. I decided to adopt this instrument as it was credible, and helped to measure both personal and pedagogical-didactic skills of teachers. A total of 44 items indicated in a questionnaire were grouped into 3 factors, whereby scale reliability statistics showed mean = 3.20, standard deviation = .78, Cronbach’s α = .97, and McDonald’s ω= .97. Exploratory factor analysis (EFA) with Varimax rotation was conducted using data. All factors showed a sufficient to good internal consistency (Field, 2013) and content reliability. Throughout the study I ensured ethical consideration, whereby the participants took part in research on a voluntary basis and signed a written consent form, which clearly stipulated their rights to withdraw at any time of the research. I also coded participants’ details and safeguarded the data in my computer through setting passcodes. The University also sent an official letter to local educational departments to grant access to schools and inform about the potential outcome of the study.
Conclusions, Expected Outcomes or Findings
The qualitative data results indicate that teachers are concerned about the importance of including ML components in their day-to-day lessons, though they highlight certain barriers. The challenges that teachers experience with ML include selecting appropriate resources, teaching methods as well as attitudes of other stakeholders of education. Four major themes were identified from the qualitative data analysis: (1) evaluating the validity of media messages and assessing them; (2) communicating media messages; and (3) safety; (4) ML practice in the classroom and ethics in pedagogy.
Teacher’s practice of media literacy varied based on their preparation and policy guidelines in their respective schools. The learning curve was facilitated and supported: ‘I know that our republic in 2012 started the work on facilitating the formation of literacy in the field of media education’ (Teacher_1). However, teachers criticized the lack of practice and post-course support ‘However, students learn how to think critically about media through practice. We do not have much practice in media literacy’ (Teacher_9).
The key findings from the questionnaire show that teachers’ personal competencies rated higher compared to pedagogical-deductive ones. Most of the teachers could operate different media devices in a technical sense (n=76), they could choose them consciously based on different functions (n=74), but the confidence in using Artificial Intelligence for educational purposes was lower (n=62).
Overall the study outcomes indicate that teachers have a general understanding of ML and are willing to facilitate students’ ML skills in their classrooms. However, there is little to no policy or guidance for teachers to promote ML in their classrooms in an ethical manner.
References
Brunner, C., & Tally, W. (1999). The new media literacy handbook: An educator's guide to bringing new media into the classroom. Doubleday.
Creswell, J. W. (2014). A concise introduction to mixed methods research. SAGE publications.
Darling-Hammond, L. (2000). Teacher quality and student achievement. Education policy analysis archives, 8, 1-1.
Felini Ed D, D. (2014). Quality Media Literacy Education. A Tool for Teachers and Teacher Educators of Italian Elementary Schools. Journal of Media Literacy Education, 6(1), 3.
Fullan, M. (1982). The meaning of educational change. Toronto: OISE press.
Gambrell, L. B., Malloy, J. A., & Mazzoni, S. A. (2011). Evidence-based best practices for comprehensive literacy instruction. Best practices in literacy instruction, 4, 11-56.
Hargreaves, A. & Evans, R. (1997). Teachers and educational reform. In Hargreaves, A. and Evans, R. (Eds.) Beyond Educational Reform: bringing teachers back in. Buckingham: Open University Press.
Hill, J. (2022). Policy responses to false and misleading digital content: A snapshot of children’s media literacy.
Ingvarson, L., Meiers, M., & Beavis, A. (2005). Factors affecting the impact of professional development programs on teachers' knowledge, practice, student outcomes & efficacy.
Korona, M. (2020). Evaluating online information: Attitudes and practices of secondary English Language Arts teachers. Journal of Media Literacy Education, 12(1), 42–56. https://doi.org/10.23860/jmle-2020-12-1-4
Manfra, M., Holmes, C. (2020). Integrating media literacy in social studies teacher education. Contemporary Issues in Technology and Teacher Education, 20(1), 121-141
Mason, L. (2016). McLuhan's challenge to critical media literacy: The City as Classroom textbook. Curriculum inquiry, 46(1), 79-97.
Meehan, J., Ray, B., Wells, S., Walker, A., & Schwarz, G. (2015). Media literacy in teacher education: A good fit across the curriculum. Journal of Media Literacy Education. https://doi.org/10.23860/jmle-7-2-8
Murray, J. (2021). Literacy is inadequate: young children need literacies. International Journal of Early Years Education, 29(1), 1-5.
OECD (Organisation for Economic Co-operation and Development). 2020b. Early Learning and Child Well-Being: A Study of Five-Year-Olds in England, Estonia, and the United States. https://doi.org/10.1787/3990407f-en
Pederson, R. (2023). An Argument for Including Critical Media Literacy in EFL Curriculum and Pedagogy. English Teaching, 78(1).
Reimers, F. (2009). 14 Educating for Global Competency. International perspectives on the goals of universal basic and secondary education, 22, 183-202.
Robertson, L., &; Hughes, J.M. (2011). Investigating pre-service teachers’ understandings of critical media literacy. Language and Literacy, 13(2), 37-53.
Paper
Classroom Situations As Knowledge Construction With Digital Media
Goethe-University Frankfurt am Main, Germany
Presenting Author:Digital infrastructure as a media environment must be understood as embedded in social processes and spatial structures. Considering measures aimed at digitalisation of schools at the knowledge and infrastructure level and practices teachers and pupils develop in building digitally enhanced environments in the classroom, questions about changes to teaching situations arise. Situational analysis (Clarke et al., 2022) allows research on the complexity of spatial-material and communicative-discursive networks. Taking digitally extended learning environments as an example, combinations of situation-analytical mappings prove helpful in depicting interactions of social actors and nonhuman actants (Clarke et al., 2022, p. 12) and their positioning in constructing teaching and learning situations.
Digital artifacts and their implementation and integration in classroom practices are at the core of recent discourses in education. Measures of saturating institutionalised pedagogical contexts (School, University) with digital technologies. In the European context the examples of Austria, Germany and Switzerland (BMBWF, 2018; Educa, 2021; KMK, 2021) show measures aiming at three levels:
- Expansion of digital infrastructure (equipping schools, school staff and pupils with digital devices) and its management (creation of administration units and platforms)
- Redefinition of new knowledge structures for teaching (competency frameworks, curriculum reforms)
- Restructuring of teaching through teacher training and further education regarding the use of digital teaching/learning materials and corresponding models of teaching and learning
This meets structural indicators of curriculum, teachers, assessment and a so-called “digital education ecosystem” (Eurydice, 2023) for digital change at the European level. Initiatives to digitalize institutionalized pedagogical spaces provide specific infrastructures. These infrastructures are inscribed with certain ways of acting and convey particular ways of knowledge construction into classroom situations. Also, digital devices like smartphones as always available technological artefacts shape everyday classroom practice not planned by administrative measures. The ways teachers engage with the learning environment and use options of providing and communicating the use of digital media could be planned (cf. Petko, 2020; Schmid et al., 2020). In this process, “a specific teaching and learning environment” (Petko, 2020, p. 115) is constructed. There is still little research on spontaneous situations that arise in the classroom without having planned the use of technical devices in advance.
Discourses of progress associated with digitalisation promote assumptions of teaching situations being “improved” by digital artifacts (Selwyn, 2022, p. 26f). The paper discusses how educational research may be inspired by Science and Technology Studies. Technical artifacts are analysed as part of knowledge construction (Wyatt, 2008) and teaching is understood as an institutionalized and professionalised “situation” (Terhart, 2009, p. 103) of normative character (Hollstein et al., 2016, p. 44) in the classroom as a socially and communicatively constructed space (Christmann, 2022; Knoblauch & Steets, 2022). The paper aims at developing an informed position by discussing technological determinism (Wyatt, 2008) and how it is enacted in the ways teachers select and position technology and technological artefacts in the classroom. Therefore, the guiding question of the paper is how digital artefacts are used in classroom situations and how they are situated as artefacts in the course of knowledge construction.
Methodology, Methods, Research Instruments or Sources Used
Drawing on situational analysis (Friese, 2023), and inspirations from Science and Technology Studies (Hackett et al., 2008) the idea of following artifacts – as opposed to following the actors (Wyatt, 2008, p. 170) – is taken up. Complexities of teaching in classrooms as socio-technical situations will be analysed so as to better understand and challenge ways of thinking about school and knowledge (cf. Lynch, 2008, p. 10). Classroom practices and the construction of digitally enriched learning environments is often linked to planning classroom settings. At present, schools have very different conditions for digital teaching. It is therefore not possible to assume “stable, circumscribed situations” (Friese, 2023, p. 115). Given different starting conditions, the classroom infrastructure and digital artefacts as a constitutive element in the creation of situations move to the centre of observation. Especially their role in established classroom practices of knowledge construction help to identify, if proclaimed changes or progresses are made and what role they actually play in teaching and learning.
Situational analysis and analytical maps are used in order to reconstruct situation-specific discourses, arenas and positions (Clarke, 2016). Focusing on digital artefacts in use in the classroom, the paper draws on the four possible kinds of maps exemplified by Clarke et al. (Clarke et al., 2022), situational maps help to identify “major human, nonhuman, discursive, historical, symbolic, cultural, political and other elements” (Clarke et al., 2022, p. 10) and identify key elements to be mapped in relational maps that “explore relations among all the key elements” (Clarke et al., 2022, p. 13). Especially for detangling “social, organizational and institutional dimensions of the situation”, social worlds/arenas are key elements in the analysis of classroom situations, distinguishing the social world inside classroom walls from the social arena of school for instance (Clarke et al., 2022, p. 14). Positional maps shed light on discursive positions in the situation and lay out “axes of concern and controversy” (Clarke et al., 2022, p. 15) enabling a differentiated look at knowledge as constructed issue in teaching situations. Characteristically, all four kinds of maps take nonhuman actants “seriously as active, coconstitutive elements” (Clarke et al., 2022, p. 15). Questions of where and how digital elements are placed in learning environments and how discourses and dynamics are developed in relation to their placing are therefore met with this methodological approach. This opens up new perspectives on educational media research on teaching and digital media.
Conclusions, Expected Outcomes or Findings
The following example is a social science class in a fifth grade. In this class personal smartphones of pupils turn into artifacts of engagement with an exhibition about school back in time, today and tomorrow organised within the school building.
As soon as technical artefacts are located or placed in the physical learning space, presuppositions about their role in knowledge construction are enacted. Situated opportunities of action are realised by teachers and learners within the classroom situation in relation to the spatial-technical-social environment. In the example pupils use their smartphones to take photos and videos of the exhibits. The sequence offers potential to take a closer look at media and digital media placed within the infrastructure of the lesson and ways of interaction by different actors. Practices range from distancing to engaging with the exhibits. Different ways of knowledge construction enfold as pupils interact with each other as well as the exhibits and their personal smartphones, producing media-representations of their experience. Questions of knowledge construction through media engagement, power and participation, connected to digital artefacts could be transferred to platforms, software solutions and digital teaching materials. But the focus will shift from effects of technologies on teaching towards processes of knowledge construction in specific situations, of use and placement of digital artefacts in classroom interactions.
Following artefacts and asking for how they are communicatively integrated in knowledge construction in classroom situations proves useful with regard to complex structures and varying technical arrangements, social roles and practices. Situation analysis brings implicit aspects to the surface in order to better understand the relationship between education and its technology.
References
BMBWF. (2018). Masterplan für die Digitalisierung im Bildungswesen (Digitale Schule). https://www.bmbwf.gv.at/Themen/schule/zrp/dibi/mp.html
Christmann, G. B. (2022). The theoretical concept of the communicative (re)construction of spaces. In G. B. Christmann, M. Löw, & H. Knoblauch (Eds.), Communicative Constructions and the Refiguration of Spaces (1st ed., pp. 89–112). Routledge.
Clarke, A. E. (2016). Situational Analysis. In The Blackwell Encyclopedia of Sociology (pp. 1–2). John Wiley & Sons, Ltd.
Clarke, A. E., Washburn, R., & Friese, C. (2022). Situational Analysis in Practice: Mapping Relationalities Across Disciplines. Routledge.
Educa. (2021). Digitalisierung in der Bildung. (p. 334). Fachagentur für den digitalen Bildungsraum Schweiz. https://www.educa.ch/de/news/2021/bericht-digitalisierung-der-bildung
Eurydice. (2023). Structural indicators for monitoring education and training systems in Europe 2023: Digital competence at school. Publications Office of the EU. https://data.europa.eu/doi/10.2797/886074
Friese, C. (2023). Situational Analysis and Digital Methods. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 24(2), Article 2. https://doi.org/10.17169/fqs-24.2.4078
Hackett, E. J., Amsterdamska, O., Lynch, M., & Wajcman, J. (Eds.). (2008). The handbook of science and technology studies (3rd ed). MIT Press.
Hollstein, O., Meseth, W., & Proske, M. (2016). „Was ist (Schul)unterricht?“: Die systemtheoretische Analyse einer Ordnung des Pädagogischen. In T. Geier & M. Pollmanns (Eds.), Was ist Unterricht? (pp. 43–75). Springer.
KMK. (2021). Lehren und Lernen in der digitalen Welt. Ergänzung zur Strategie der Kultusministerkonferenz „Bildung in der digitalen Welt“ (09.12.2021).
Knoblauch, H., & Steets, S. (2022). From the constitution to the communicative construction of space. In G. B. Christmann, M. Löw, & H. Knoblauch, Communicative Constructions and the Refiguration of Spaces (pp. 19–35). Routledge.
Lynch, M. (2008). Ideas and Perspectives. In E. J. Hackett & Society for Social Studies of Science (Eds.), The handbook of science and technology studies (pp. 9–12). MIT Press.
Petko, D. (2020). Einführung in die Mediendidaktik: Lehren und Lernen mit digitalen Medien (2. Auflage). Beltz.
Schmid, M., Brianza, E., & Petko, D. (2020). Developing a short assessment instrument for Technological Pedagogical Content Knowledge (TPACK.xs) and comparing the factor structure of an integrative and a transformative model. Computers & Education, 157, 103967. https://doi.org/10.1016/j.compedu.2020.103967
Terhart, E. (2009). Didaktik: Eine Einführung. Reclam.
Wyatt, S. (2008). Technological Determinism Is Dead; Long Live Technological Determinism. In E. J. Hackett, O. Amsterdamska, M. Lynch, J. Wajcman, & Published in cooperation with the Society for the Social Studies of Science (Eds.), The Handbook of Science and Technology Studies (3rd ed, pp. 165–180). MIT Press.