The Refined Consensus Model of Pedagogical Content Knowledge (PCK): Detecting Filters Between the Realms of PCK

In this article, we analyse potential filters that moderate the transformation process between the realms of PCK defined in the refined consensus model of pedagogical content knowledge. We tested 58 preservice biology teachers in a 15-week one-group pretest/post-test design. To identify filters between collective PCK (cPCK) and personal PCK (pPCK), we set up moderation models with pretest pPCK as an independent variable, post-test pPCK as a dependent variable, and motivational orientations or professional values as moderator variables. To identify filters between pPCK and enacted PCK (ePCK), we set up moderation models with post-test pPCK as an independent variable, ePCK as a dependent variable, and noticing or knowledge-based reasoning as moderator variables. We did this specifically with a focus on language in biology education. We found that only the variable knowledge-based reasoning had a role as a filter. It moderates the transformation process between pPCK and ePCK (moderation analysis: F(3,19) = 10.40, p < 0.001, predicting 25.72% of the variance). In future studies, other filters should be identified

Using the Plan–Teach–Reflect Cycle of the Refined Consensus Model of PCK to Improve Pre-Service Biology Teachers’ Personal PCK as Well as Their Motivational Orientations

In this article, we analyse how to improve pre-service biology teachers’ pPCK (personal Pedagogical Content Knowledge), professional values and motivational orientations in the field of academic and scientific language. On the basis of the theory of the Refined Consensus Model of PCK (RCM), we made a two-month quasi-experimental intervention study with 32 pre-service biology teachers. As a treatment, we trained the participants in the Plan–Teach–Reflect Cycle of enacted PCK in a school class, in the framework of a seminar. In the control group, the teaching of the cycle was replaced by presentations of their lesson plans. As dependent variables, we analysed participants’ pPCK, professional values and motivational orientations. Our results showed an increase in pre-service biology teachers’ pPCK (F(1,28) = 3.51, p = 0.04, part. η2 = 0.11, d = 0.70) and motivational orientations (F(1,23) = 29.68, p < 0.01, part. η2 = 0.56, d = 2.26) in both groups, but no effects on participants’ professional values. The teaching experience in a school class strengthened the effects both in participants’ pPCK (F(1,28) = 2.92, p = 0.04, part. η2 = 0.10, d = 0.67) and motivational orientations (F(1,23) = 7.64, p < 0.01, part. η2 = 0.25, d = 1.15). We recommend integrating the use of the Plan–Teach–Reflect Cycle of ePCK into science teacher education programmes

The Refined Consensus Model of Pedagogical Content Knowledge (PCK): Detecting Filters between the Realms of PCK

In this article, we analyse potential filters that moderate the transformation process between the realms of PCK defined in the refined consensus model of pedagogical content knowledge. We tested 58 preservice biology teachers in a 15-week one-group pretest/post-test design. To identify filters between collective PCK (cPCK) and personal PCK (pPCK), we set up moderation models with pretest pPCK as an independent variable, post-test pPCK as a dependent variable, and motivational orientations or professional values as moderator variables. To identify filters between pPCK and enacted PCK (ePCK), we set up moderation models with post-test pPCK as an independent variable, ePCK as a dependent variable, and noticing or knowledge-based reasoning as moderator variables. We did this specifically with a focus on language in biology education. We found that only the variable knowledge-based reasoning had a role as a filter. It moderates the transformation process between pPCK and ePCK (moderation analysis: F(3,19) = 10.40, p &lt; 0.001, predicting 25.72% of the variance). In future studies, other filters should be identified

Retrospective analysis of recurrence patterns and clinical outcome of grade II meningiomas following postoperative radiotherapy

BACKGROUND: Atypical meningiomas exhibit a high tendency for tumor recurrence even after multimodal therapy. Information regarding recurrence patterns after additive radiotherapy is scarce but could improve radiotherapy planning and therapy decision. We conducted an analysis of recurrence patterns with regard to target volumes and dose coverage assessing target volume definition and postulated areas of tumor re-growth origin. Prognostic factors contributing to relapse were evaluated. METHODS: The clinical outcome of patients who had completed additive, somatostatin receptor (SSTR)-PET/CT-based fractionated intensity-modulated radiotherapy for atypical meningioma between 2007 and 2017 was analyzed. In case of tumor recurrence/progression, treatment planning was evaluated for coverage of the initial target volumes and the recurrent tumor tissue. We proposed a model evaluating the dose distribution in postulated areas of tumor re-growth origin. The median of proliferation marker MIB-1 was assessed as a prognostic factor for local progression and new distant tumor lesions. RESULTS: Data from 31 patients who had received adjuvant (n = 11) or salvage radiotherapy (n = 20) were evaluated. Prescribed dose ranged from 54.0 to 60.0 Gy. Local control at five years was 67.9%. Analysis of treatment plans of the eight patients experiencing local failure proved sufficient extent of target volumes and coverage of the prescribed dose of at least 50.0 Gy as determined by mean dose, D98, D2, and equivalent uniform dose (EUD) of all initial target volumes, postulated growth-areas, and areas of recurrent tumor tissue. In all cases, local failure occurred in high-dose volumes. Tumors with a MIB-1 expression above the median (8%) showed a higher tendency for re-growth. CONCLUSIONS: The model showed adequate target volume and relative dose distribution but absolute dose appears lower in recurrent tumors without reaching statistical significance. This might provide a rationale for dose escalation studies. Biological factors such as MIB-1 might aid patients’ stratification for dose escalation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-021-01825-2