Temporal Network Analysis of Small Group Discourse

The analysis of school-age children engaged in engineering projects has proceeded by examining the conversations that take place among those children. The analysis of classroom discourse often considers a conversational turn to be the unit of analysis. In this study, small-group conversations among students engaged in a robotics project are analyzed by forming a dynamic network with the students as nodes and the utterances of each turn as edges. The data collected for this project contained more than 1000 turns for each group, with each group consisting of 4 students (and the occasional inclusion of a teacher or other interloper). The conversational turns were coded according to their content to form edges that vary qualitatively, with the content codes taken from prior literature on small group discourse during engineering design projects, resulting in approximately 10 possible codes for each edge. Analyzed as a time sequence of networks, clusters across turns were created that allow for a larger unit of analysis than is usually used. These larger units of analysis are more fruitfully connected to the stages of engineering design. Furthermore, the patterns uncovered allow for hypotheses to be made about the dynamics of transition between these stages, and also allow for these hypotheses to be compared to expert consideration of the group’s stage at various times. Although limited by noise and inter-group variation, the larger units allowed for greater insight into group processes during the engineering design cycle

Design of a Problem-Based Learning Pain and Palliative Care Elective Course

Objective To implement and evaluate a problem-based learning (PBL) pain and palliative care elective course to develop studentsʼ pain and symptom management pharmacotherapy knowledge, clinical reasoning process, and self-directed learning skills. Methods Each week students received a patient case to independently develop an assessment and plan for each pain and symptom management problem. During class the students discussed their findings within small groups in preparation for a large-group discussion with the instructor. Studentsʼ course grades were based on weekly pre-class case preparation, individual case studies, and self-reflection questions. To assess knowledge gained over the semester a free-response pre- and post-course test was given. Results Twenty-five students enrolled in this course. A t-test comparison of the pre- and post-tests yielded a significant difference between the pre- and post-test scores (p \u3c 0.001), with the mean score for the tests increasing from 9.6 (out of 20 points) on the pre-test to 14.1 on the post-test. Pearsonʼs correlation coefficient between the pre- and post-test was 0.45, indicating increased scores were not a result of improvement only among the strong students. The normalized gain \u3cg\u3e was 0.43. The average score for each individual case study was slightly more than 80%. Four themes were noted in the studentsʼ self-reflections including patient/family goals of care, individualization of patient care and contrast to curative treatment, improved comfort with “gray therapeutic areas,” and advantages and disadvantages of problem-based learning. Conclusions Students demonstrated improved pain and symptom management pharmacotherapy knowledge, clinical reasoning process, and self-directed learning skills after course completion. The skills developed by students will benefit them in future clinical practice. Additional studies are needed to assess the long-term impact of the skills developed in this course

Graph Theoretic Methods for the Analysis of Data in Developing Systems

A full examination of learning or developing systems requires data analysis approaches beyond the commonplace pre-/post-testing. Drawing on graph theory, three particular approaches to the analysis of data—based on adjacency matrices, affiliation networks, and edit distances—can provide additional insight into data; these methods are applied to student performance in a Calculus course. Data analysis methods based on adjacency matrices demonstrate that learning is not unidimensional, that learning progressions do not always progress monotonically toward desired understandings and also provide insight into the connection between instruction and student learning. The use of affiliation networks supports the concept development theory of Lev Vygotsky and also provides insight into how students’ prior knowledge relates to topics being studied. Careful use of edit distances indicates a likely overestimate of effect sizes in many studies, and also provides evidence that concepts are often created in an ad hoc manner. All of these have implications for curriculum and instruction, and indicate some directions for further inquiry

State Space Analysis and its Connection to the Classroom

Discrete dynamical systems have been used to theoretically model the complex dynamics of classrooms. While time-series analyses of these models has yielded some insights, state space analyses can yield additional insights; this paper will explore state space analyses and their application to classroom situations. One benefit of state space analysis is that it allows simultaneous exploration of multiple time-series, and so can more easily provide information about divergence and convergence of paths. Additionally, state space analysis, more easily than time-series analysis, can provide information about the existence of multiple paths leading toward a desired state. Further, state space analysis can identify different regimes of behaviors, finding boundaries near which there may be divergent behaviors, and also using those regimes to define a (sometimes) relatively small number of archetypical behaviors. This is particularly useful in tracking behaviors at a microgenetic level, since multiple initial conditions may get to the same (or very close) final states, but in dramatically different ways, and these different routes may have implications for future classroom experiences. Because of these advantages, state space analysis can be used to inform attempts at differentiated instruction in a classroom, assist modelers in identifying appropriate parameter scales, and provide guidance for empirical studies of classroom learning. These ideas will be illustrated through state space analysis of an existing model of teacher-student interactions, identifying four regimes of behaviors, and leading to several implications for classroom practice and research

Targeting Social Connection in the Context of Trauma: Functional Outcomes and Mechanisms of Change

The current study presents and preliminarily tests a brief, theory driven intervention designed to target social connectivity as a transdiagnostic mechanism of health. We tested four hypotheses to examine whether and how explicitly targeting social behavior engagement (activating values-led behaviors towards specific network members) may improve other downstream aspects of social connectivity (i.e., social cognitions measured as loneliness, interpersonal closeness, perceived social support) and functioning (quality of life [QOL] and posttraumatic stress symptoms [PTS]). Methods. Participants included 15 patients (10 veterans, 5 firefighters) who completed the six-session intervention. Demographics: age (M = 46, SD = 17), 87% male, race (80% Caucasian, 20% Hispanic), 60% married/partnered, 47% living alone. Our multi-analytic approach included parametric and non-parametric tests: (a) significance testing and effect sizes to examine whether variables of interest changed, and (b) Granger causality analysis of repeated measures to examine the mechanistic theory of change (does social behavior engagement lead to improved social cognition and functioning?). Results. Statistically significant, medium-large effect size improvements were shown for QOL (Cohens d = 1.05), PTS (d = 1.05), social behavior engagement (d = 0.78), and several social cognitions (loneliness, d = 0.80, interpersonal closeness, d = 0.53). Models accounted for medium-large variance explained in improved QOL (R2 = 0.47, 95% CI [0.00,0.66]) and PTS (R2 = 0.56, 95% CI[0.07,0.72]). The theory of change was supported, with increase in social behaviors preceding improvement in social cognitions (not vice-versa). Conclusions. Improving social connectivity is a mechanism for improving QOL and mental health. Focus on initiating values-driven social behaviors may be an efficient and effective entry point to stimulate change

A Study of Time-Dependent CP-Violating Asymmetries and Flavor Oscillations in Neutral B Decays at the Upsilon(4S)

We present a measurement of time-dependent CP-violating asymmetries in neutral B meson decays collected with the BABAR detector at the PEP-II asymmetric-energy B Factory at the Stanford Linear Accelerator Center. The data sample consists of 29.7 ${\rm fb}^{-1}$ recorded at the $\Upsilon(4S)$ resonance and 3.9 ${\rm fb}^{-1}$ off-resonance. One of the neutral B mesons, which are produced in pairs at the $\Upsilon(4S)$, is fully reconstructed in the CP decay modes $J/\psi K^0_S$, $\psi(2S) K^0_S$, $\chi_{c1} K^0_S$, $J/\psi K^{*0}$ ($K^{*0}\to K^0_S\pi^0$) and $J/\psi K^0_L$, or in flavor-eigenstate modes involving $D^{(*)}\pi/\rho/a_1$ and $J/\psi K^{*0}$ ($K^{*0}\to K^+\pi^-$). The flavor of the other neutral B meson is tagged at the time of its decay, mainly with the charge of identified leptons and kaons. The proper time elapsed between the decays is determined by measuring the distance between the decay vertices. A maximum-likelihood fit to this flavor eigenstate sample finds $\Delta m_d = 0.516\pm 0.016 {\rm (stat)} \pm 0.010 {\rm (syst)} {\rm ps}^{-1}$. The value of the asymmetry amplitude $\sin2\beta$ is determined from a simultaneous maximum-likelihood fit to the time-difference distribution of the flavor-eigenstate sample and about 642 tagged $B^0$ decays in the CP-eigenstate modes. We find $\sin2\beta=0.59\pm 0.14 {\rm (stat)} \pm 0.05 {\rm (syst)}$, demonstrating that CP violation exists in the neutral B meson system. (abridged)Comment: 58 pages, 35 figures, submitted to Physical Review

Measurement of the Branching Fraction for B- --> D0 K*-

We present a measurement of the branching fraction for the decay B- --> D0 K*- using a sample of approximately 86 million BBbar pairs collected by the BaBar detector from e+e- collisions near the Y(4S) resonance. The D0 is detected through its decays to K- pi+, K- pi+ pi0 and K- pi+ pi- pi+, and the K*- through its decay to K0S pi-. We measure the branching fraction to be B.F.(B- --> D0 K*-)= (6.3 +/- 0.7(stat.) +/- 0.5(syst.)) x 10^{-4}.Comment: 7 pages, 1 postscript figure, submitted to Phys. Rev. D (Rapid Communications

Evidence for the Rare Decay B -> K*ll and Measurement of the B -> Kll Branching Fraction

We present evidence for the flavor-changing neutral current decay $B\to K^*\ell^+\ell^-$ and a measurement of the branching fraction for the related process $B\to K\ell^+\ell^-$, where $\ell^+\ell^-$ is either an $e^+e^-$ or $\mu^+\mu^-$ pair. These decays are highly suppressed in the Standard Model, and they are sensitive to contributions from new particles in the intermediate state. The data sample comprises $123\times 10^6$ $\Upsilon(4S)\to B\bar{B}$ decays collected with the Babar detector at the PEP-II $e^+e^-$ storage ring. Averaging over $K^{(*)}$ isospin and lepton flavor, we obtain the branching fractions ${\mathcal B}(B\to K\ell^+\ell^-)=(0.65^{+0.14}_{-0.13}\pm 0.04)\times 10^{-6}$ and ${\mathcal B}(B\to K^*\ell^+\ell^-)=(0.88^{+0.33}_{-0.29}\pm 0.10)\times 10^{-6}$, where the uncertainties are statistical and systematic, respectively. The significance of the $B\to K\ell^+\ell^-$ signal is over $8\sigma$, while for $B\to K^*\ell^+\ell^-$ it is $3.3\sigma$.Comment: 7 pages, 2 postscript figues, submitted to Phys. Rev. Let

Study of e+e- --> pi+ pi- pi0 process using initial state radiation with BABAR

The process e+e- --> pi+ pi- pi0 gamma has been studied at a center-of-mass energy near the Y(4S) resonance using a 89.3 fb-1 data sample collected with the BaBar detector at the PEP-II collider. From the measured 3pi mass spectrum we have obtained the products of branching fractions for the omega and phi mesons, B(omega --> e+e-)B(omega --> 3pi)=(6.70 +/- 0.06 +/- 0.27)10-5 and B(phi --> e+e-)B(phi --> 3pi)=(4.30 +/- 0.08 +/- 0.21)10-5, and evaluated the e+e- --> pi+ pi- pi0 cross section for the e+e- center-of-mass energy range 1.05 to 3.00 GeV. About 900 e+e- --> J/psi gamma --> pi+ pi- pi0 gamma events have been selected and the branching fraction B(J/psi --> pi+ pi- pi0)=(2.18 +/- 0.19)% has been measured.Comment: 21 pages, 37 postscript figues, submitted to Phys. Rev.

Measurement of Branching Fraction and Dalitz Distribution for B0->D(*)+/- K0 pi-/+ Decays

We present measurements of the branching fractions for the three-body decays B0 -> D(*)-/+ K0 pi^+/-$and their resonant submodes$B0 -> D(*)-/+ K*+/- using a sample of approximately 88 million BBbar pairs collected by the BABAR detector at the PEP-II asymmetric energy storage ring. We measure: B(B0->D-/+ K0 pi+/-)=(4.9 +/- 0.7(stat) +/- 0.5 (syst)) 10^{-4} B(B0->D*-/+ K0 pi+/-)=(3.0 +/- 0.7(stat) +/- 0.3 (syst)) 10^{-4} B(B0->D-/+ K*+/-)=(4.6 +/- 0.6(stat) +/- 0.5 (syst)) 10^{-4} B(B0->D*-/+ K*+/-)=(3.2 +/- 0.6(stat) +/- 0.3 (syst)) 10^{-4} From these measurements we determine the fractions of resonant events to be : f(B0-> D-/+ K*+/-) = 0.63 +/- 0.08(stat) +/- 0.04(syst) f(B0-> D*-/+ K*+/-) = 0.72 +/- 0.14(stat) +/- 0.05(syst)Comment: 7 pages, 3 figures submitted to Phys. Rev. Let