Due to ongoing digitalisation, Web searching competency has become a key factor for active participation in the 21st-century global knowledge society. The skillful use of search engines and innumerable sources of textual, graphic, and audiovisual online information has become indispensable for meeting the challenges of everyday life, from the simple need to look up the train schedule, a foreign word, etc. through to thoroughly acquainting oneself with a specific topic in educational or professional contexts. In secondary school, when the information-oriented use of the Internet becomes crucial for completing assignments, students tend to spend more and more time searching the Web (mpfs 2018). Nevertheless, existing research shows a lack of digital information literacy among German secondary students (Eickelmann et al. 2019). During the Corona pandemic, it has become even more obvious that the German school system has a lot to catch up on in this field. Not only does the backlog relate to missing or deficient technical equipment for both students and teachers, but also to inadequate didactic approaches (Gerhardts et al. 2020). While the provision of an up-to-date technical infrastructure is mainly a matter of prioritisation in educational policy, the latter problem should be systematically addressed by scientific research. Focusing on Web searching competency, one finds that this essential subdomain of digital information literacy is still marginalised in German school curricula and teaching. To optimise curricula and ensuing didactic decision-making in this regard, we first need a clear understanding of this learning area, i. e. a scientifically sound competency model upon which to base all further discussion. Reviewing the literature, one can find some scientific publications on modelling digital information literacy for the purpose of competency testing at secondary level (Balceris 2011; Eickelmann et al. 2019). However, there is a research gap concerning a more specific and nuanced kind of modelling Web searching competency, which is explicitly aimed at didactic uses.

Cross-country comparisons based on international large-scale assessments of digital information literacy reveal that German secondary students have stagnated in a mid-table position for years (Bos et al. 2014; Eickelmann et al. 2019). As to the subdomain of Web searching competency, constructing a competency model with an explicit didactic scope, suitable to inform curricular changes and the development of progressive didactic designs, should not only be helpful for German reform efforts in this learning area but for respective reforms in other middle- or low-ranking countries, too. Such a model might even substantiate further quality improvement in the higher-ranking countries. Likewise, a scientifically recognised competency model could establish common ground for transnational expert discourse.

The poster gives an overview on a doctoral research study aiming at the construction of a didactically useful model of Web searching competency at secondary school level (cf. Gerhardts in prep.). Competency modelling generally comprises two fundamental steps (Schaper 2009): Firstly, defining the components of the model; and, secondly, defining its dimensional structure. So the two central questions underlying this research are:

1) What are the relevant sub-competencies that secondary school students should develop to perform competent Web searches?

2) How can all these itemised sub-competencies be bunched up to larger units serving didactic purposes?

Starting from pre-existing theoretical concepts of (digital) information literacy (Coiro et al. 2008; Balceris 2011; Eickelmann et al. 2019) and theories about self-regulated information processing (Winne/Hadwins 1998), from subject-oriented learning theory (Holzkamp 1995), and special theories about learning in hypermedia environments (Bannert 2007; Gerjets 2017), it shows that the intended sort of competency model requires in-depth empirical research.

Modelling Web searching competency to improve educational practices should be based on empirical data providing information on typical school-related search processes that are close to the students’ authentic procedures. With this in mind, several qualitative methods were combined for data collection. During a first session, 20 secondary students (socio-demographically heterogeneous volunteers from three local schools) were asked to perform a 45-minute Web search. All students were given the same exemplary search task and a laptop for individual use. The students were advised to think out loud while searching for information, i. e. to spontaneously verbalise everything crossing their minds without any further reflection. A screen video of the search process, a user video, and the soundtrack of the concomitant think-aloud were recorded by a special software, which had been pre-installed on the laptop. The second session consisted of a stimulated recall and a qualitative interview, both with the same subsample of six students (systematically chosen according to the criterion of maximal contrast as to the “nature” of their recently recorded Web search). Thus, various approaches to solving the search task (i. e. assumably a wide range of different aspects of Web searching competency) were to be covered. After the first screening of all the collected Web search data, taking an in-depth look at a subsample of six markedly differing students seemed to be reasonable both in terms of saturation and work efficiency. While in the first part of this session the participants were given the opportunity to comment freely on the video of their search process, the second part enabled the interviewer to find out more about so far insufficiently specified aspects. Prior to analysis, all sorts of data were transcribed and put together in one table providing a “multi-data” synopsis. Methods of data analysis suitable for this study have to fulfill two separate requirements: firstly, to break down the complex search process data in such a way as to identify all components of Web searching competency relevant at secondary school level (step 1 of competency modelling); and, secondly, to reorganise the identified components in terms of didactically purposeful dimensions (step 2 of competency modelling). Accordingly, two qualitative methods of data analysis are combined here. In the first step, qualitative content analysis has been applied to all the collected data in their entirety. As a second step, subsample data is presently coded following the techniques of Grounded Theory Methodology.

Having completed the first step of competency modelling, an important methodical insight is that triangulating three different methods of data collection proved appropriate to gain both comprehensive and granular information on secondary students’ typical Web search processes. It has shown that each kind of collected data can compensate for missing or ambiguous information in the other ones. For example, screencast/think-aloud data reveal whole sequences of actions, but verbalisations are naturally incomplete and often ambiguous. While stimulated recalls are especially suited to reduce blank gaps in certain parts of the video-/audiotaped Web search, qualitative interviews provide trans-situational information and subjective views on Web searching in general. Thus, the qualitative content analysis of all these kinds of data has yielded a vast set of components of Web searching competency relevant at secondary level. For validation purposes, data were coded twice in terms of intracoder reliability. The second step of competency modelling is still in progress. Subsample data are densified by generating theoretical codes and incrementally integrating them so as to come up with a reduced number of pivotal dimensions. The expected outcome is a model of Web searching competency, which is "didactically grounded" in that it incorporates the secondary students’ perspective on how this learning area could be modulated to be perceived as meaningful and accessible by the learners themselves. At this point, as a result of several rounds of analytic densification, eleven dimensions are distinguished: (1)Preparing the Web search; (2)Controlling time and target fulfilment; (3)Utilising navigation and search tools; (4)Applying strategies of orientation; (5)Controlling comprehension; (6)Considering relevance as a selection criterion; (7)Considering quality as a selection criterion; (8)Controlling persistence; (9)Controlling outer conditions; (10)Postprocessing the search experience; (11)Taking a responsible attitude towards web-based information. The present version of the model is well-advanced but needs to undergo further communicative validation.

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