{'fraction_non_alphanumeric': 0.02761795166858458, 'fraction_numerical': 0.01227464518603759, 'mean_word_length': 5.756476683937824, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reflections on Teachers’ Data‐Based Decision Making

Data-based decision making described in the articles in this special issue focus on how teachers, preservice teachers, and administrators make academic instructional decisions centered on student data representing an individual’s overall achievement in a particular academic area across a period of time. This process is grounded in the work of Deno and Mirkin (1977) who conceptualized and validated a system for modeling student academic growth over time that relied on simple and efficient assessment tools with sound technical characteristics, which became known as curriculum-based measurement (CBM; Deno, 1985). The purpose of this student information was to provide an objective database for making instructional decisions, especially for students who had significant learning difficulties. By tracking a student’s overall change (or lack thereof) across time, teachers could test systematically the effects of different instructional programs or techniques for individual students. When instruction was not working as anticipated, which happened often for students with disabilities, teachers could alter instructional components and use the same repeated measurement procedures to test the influence of the modified instruction. Making instructional changes for individual students based on their own patterns of progress can be seen as the essence of special education (see Deno, 1990). Although this process may appear straightforward, Fuchs (2017) described a distinction between the simple measurement tools and the more complex issue of how indicators of academic proficiency are considered when making instructional decisions. Some of these aspects of instructional decision making are addressed in this issue. Two studies, one in the United States (Espin, Wayman, Deno, McMaster, & de Rooij, 2017) and the other in the Netherlands (van den Bosch, Espin, Chung, & Saab, 2017), explored how teachers and experts in CBM interpreted student progress data. A third study in the United States compared experts’ and preservice teachers’ understanding of CBM-graphed data and examined change among preservice teachers across time (Wagner, Snidarich, Espin, Seifert, & McMaster, 2017). Differences between teachers and student teachers in Germany were examined in another study using a newly developed test of graph literacy that focused on student progress information (Zeuch, Forster, & Souvignier, 2017). An additional study in the Netherlands explored potential factors that may contribute to school differences in successful implementation of