In the past decades, much design knowledge has been gained by expert educational technology designers, and accumulated via design research projects. Learning scientists have sought various ways to make this knowledge available and useful for other educational technology designers, and particularly for novices in this field. These efforts are part of a trajectory which views education as a design-science (Collins et al. ,2004), or even more broadly, as one of the sciences of the artificial (Simon, 1969). In such fields generalizations of common examples are often articulated, to enable their application in other settings. Following the work of Alexander (1979), and his vision for articulating a “design pattern language” in architecture, learning scientists have developed several manners to articulate general design guideline for curricular design. Three main types of guidelines that have been developed are:
One example is from a group of three graduate students who designed a technology for high-school computer-science learners. The technology focused on recursive algorithms for scanning data-structure trees. One of the features the grad students designed was an animation that demonstrates a certain algorithm for scanning a tree. Users were required to solve problems that utilize the demonstrated algorithm. This feature, which initially required users to passively watch the animation, went through several revisions in this group’s design process. By the end of the course it became a manipulable tool, which enabled users to solve problems by exploring various ways to scan given trees, as well as their own trees. Analysis of their design artifacts with the concretization rubric showed that while at the beginning of the design process these students thought practically, and translated their theoretical ideas into activities; at the end of the course they did not reach a full learning environment. Their mockup was graded as 4 with the concretization rubric and included just a few activities which were fully designed, a general menu with a few navigation options, and no detailed instructions for learners. It is important to note that although they did not reach the highest level of concretization according to the rubric, they had a high pace in acquiring concretization skills.
In this slide you can see the epistemology change during the course stages. you can see the gap in students’ “theoretical” and “applied” epistemologies. At the beginning of the semester, when engaged in theoretical discourse, these students tended to advocate socio-constructivists paradigms in all dimensions, whereas when engaged in designing technologies they tended to neglect these ideas and apply more traditional approaches.On the other hand when the acquisition of concretization skills was fast the gap was reduced during the course, thus, as students developed their skills to design educational technologies, they also increased the coherence of their epistemological understanding.
Making Expert Design Knowledge Useful for Novices<br />Yael Kali<br />Technologies in Education, University of Haifa<br />Tamar Ronen-Fuhrmann<br />Transformative Learning Technologies Lab (TLTL), Stanford University<br />
Ways to articulate expert design knowledge:<br />Design narratives (Hoadley, 2002; Linn & Hsi, 2000, Mor & Noss, 2008) <br />Design principles (Herrington, 2006; Kali, 2006, 2008; Linn, Bell, & Davis, 2004; Merrill, 2002; van den Akker, 1999) <br />Design patterns (Goodyear, 2005; Goodyear & Retalis, 2010; Linn & Eylon, 2006; Mor & Winters, 2007; Retalis, Georgiakakis, & Dimitriadis, 2006) <br />These often fail to serve as useful aids for novice designers (McAndrew & Goodyear, 2007).<br />Articulating design guidelines<br />
Pedagogical model for teaching educational technology design<br />
Goal, Context and methods<br />Goal: To characterize novices’ learning processes<br />Context: 3 enactments of a “Designing Educational Technology” course (Students were teachers in middle and high schools - designed a technology-enhanced unit in their teaching field) <br />Methods:<br />14 groups (33 graduate students in education) served as case studies in a multiple case-study approach<br />Rich data: all artifacts (formal design artifacts + non formal sketches and online discussions during the design process)<br />Two rubrics for analysis (based on earlier research)<br />
High pace of maturation (concretization skill)<br />I<br />S<br />Y<br />The ISY case study<br />ISY’s module: Recursive algorithms for scanning data-structure trees<br />
Non-coherent epistemology (beginning of semester)<br />Major reducing of gap (end if semester)<br />I<br />S<br />Y<br />The ISY case study<br />
Conclusions<br />Concretization: <br />Essential skill in the process of gaining design knowledge, <br />Way to assist students to reflect and reduce gaps in their understanding about learning. <br />The pedagogical model: a productive manner for novices to use expert design knowledge (in the form of design principles and features in the DPD)to guide their design process<br />
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