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This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson observation (MLO). In MLO, it is important to understand the lesson plan in advance to clarify observation points, and we aim to develop a scale including these points.

Based on the pre-questionnaire survey, nine perspectives and two situations for MLO were extracted. From these, a questionnaire for MLO was created. The results obtained from 161 teachers were examined, and exploratory factor analysis was conducted. ANOVA was conducted to analyze the effect of differences across the duration of teaching experience on the identified factors.

We developed a self-reflection scale consisting of 14 items with three factors: [B1] focus on instructional techniques and evaluation, [B2] focus on proactive problem-solving lesson development and [B3] focus on the mathematical background of the learning content. While duration of teaching experience showed no effect, three factors of the self-reflection scale for MLO showed a significant effect. Further multiple comparisons revealed the degree of focus was [B2]>[B1]>[B3].

Teachers who use this developed scale may grasp the strengths and weaknesses of their own MLO, which leads to self-improvement. The perspectives emphasized in lesson observation are the same when creating lesson plans and implementing lessons, leading to lesson improvement. Furthermore, based on the characteristics of teachers revealed, new training programs regarding MLO can lead to higher-quality lesson studies.

The paper aims to identify the effect of ignorance of correlatedness among process observations and to implement new sampling schemes; skip and mixed sampling, in order to reduce the effect of autocorrelation on process capability index (PCI) Cpm.

Autocorrelated observations are generated using autoregressive process of order two (AR (2)) using Monte Carlo simulations. The PCI is computed based on these observations assuming the independence. The skip and mixed sampling schemes are then used to form sub-groups among correlated observations. The PCI obtained using sub-groups from skip and mixed sampling schemes are assessed using sample mean and sample standard deviation.

The paper provides empirical insights into how the effect of autocorrelation decreases in the estimated value of PCI Cpm. The use of new sampling schemes, skip and mixed sampling, reduces the effect of autocorrelation on estimates of PCI Cpm.

This paper fulfills an identified need to study how to reduce the effect of autocorrelation on PCI Cpm.

This paper introduces a novel quality measure, the percent-within-distribution, or PWD, for acceptance and payment in a quality control/quality assurance (QC/QA) performance specification (PS).

The new quality measure takes any sample size or distribution and uses a Bayesian updating process to re-estimate parameters of a design distribution as sample observations are fed through the algorithm. This methodology can be employed in a wide range of applications, but the authors demonstrate the use of the measure for a QC/QA PS with upper and lower bounds on 28-day compressive strength of in-place concrete for bridge decks.

The authors demonstrate the use of this new quality measure to illustrate how it addresses the shortcomings of the percent-within-limits (PWL), which is the current industry standard quality measure. The authors then use the PWD to develop initial pay factors through simulation regimes. The PWD is shown to function better than the PWL with realistic sample lots simulated to represent a variety of industry responses to a new QC/QA PS.

The analytical contribution of this work is the introduction of the new quality measure. However, the practical and managerial contributions of this work are of equal significance.

This study explored teachers' perceptions of a specific, collaborative peer observation structure, collegial visits, and collegial visits' connection to collective teacher efficacy (CTE). The research question was: how do teachers perceive collegial visits, particularly with respect to their influence on CTE?

Within this qualitative descriptive study, 13 K-12 educators from three northeastern USA schools (one urban high school and a suburban middle and elementary school) were interviewed individually and/or in a focus group.

Utilizing social cognitive theory as a framework for analysis, the authors found a theme of a shift from uninformed to informed perceptions of the collective. Results demonstrated that collegial visits foster positive CTE beliefs.

Since collegial visits were found to increase participants' CTE, a construct others have associated with increased student achievement, school leaders should consider implementing collegial visits as a professional learning structure in their schools after considering their specific context.

Though there has been recent scholarship connecting peer observations and CTE, there has been no research, to date, to examine the effect of the specific structure of collegial visits on CTE.

Pressures from non-profit sport organizations’ (NPSOs) external environment influence governance structures and processes. Thus, this study explores the impact of external factors on NPSO board decision making.

Using a sample of six NPSO boards (two national, four provincial/territorial), data were collected via 36 observations, 18 interviews, and over 900 documents. A thematic analysis was conducted via NVivo 12.

Results identified two external factors impacting NPSO board decision making: the sport system structure and general environment conditions. External factors impacted NPSO board decision making in terms of duration, flow, interaction, and scrutiny.

Results demonstrate the need for NPSO boards to engage in boundary-spanning activities whereby external information sources from stakeholders are incorporated to make informed decisions. Practically, NPSO boards should harness virtual meetings to continue their operations while incorporating risk management analyses to assess threats and opportunities.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

The authors examine whether limitations in Form 990 result in zero or understated fundraising and administrative expenses for organizations supported by related organizations. Form 990 does not consolidate financial information of legally separate related organizations, resulting in fundraising and administrative expenses being reported by supporting organizations but not by the supported organization.

The authors use the IRS Statistics of Income Sample Data Files and compare charities receiving support from related organizations (supported) to non-supported charities.

The authors find evidence that supported organizations are likely to report zero or understated fundraising expenses and zero administrative expenses. Those receiving related donations are more likely to have zero or understated fundraising expense while those receiving related compensation are more likely to have zero and understated fundraising and administrative expenses. The authors also find evidence that supported organizations receiving greater amounts of related donations and related compensation are also more likely to report zero and understated fundraising expenses as well as zero administrative expenses while greater amounts of related compensation are also associated with understated administrative expense.

Since donors and other stakeholders use Form 990 to evaluate nonprofits, its unconsolidated nature could result in a lack of comparability across organizations and misinformed resource allocation (e.g. donation) decisions. The results also have implications for researchers who use zero and understated fundraising and administrative expenses as proxies for low quality reporting or interpret them as data errors.

The paper examines the extent to which zero or understated fundraising expense reporting (i.e. the fundraising expense puzzle) is associated with supported organizations receiving financial support from related organizations. The authors also expand their examination to zero and understated administrative expenses.

The purpose of this case study is to determine the effectiveness of the UDL academy in terms of the number of UDL techniques used by faculty after participating in the academy and surveys to explore faculty perceptions of UDL.

This quantitative case study utilized faculty surveys about the UDL academy, class observations and review of course syllabi to determine the effectiveness of the UDL academy and explore the experience of implementing UDL.

The UDL initiative has been a positive and effective experience. Broadly, faculty have had positive perceptions of UDL implementations based on faculty surveys. The effectiveness of the UDL academy was demonstrated by the number of UDL techniques used by faculty increased significantly for faculty that participated in the UDL academy. The control group of faculty members did not increase the number of UDL techniques used based on class observations and a review of course syllabi.

This research is from one librarian's perspective since the librarian led the UDL initiative and is the sole librarian with faculty status currently at the institution. As a proponent of UDL, the librarian's perspective may be biased. Librarians can implement UDL to reduce educational barriers and support student success. Additionally, librarians can offer support to faculty in learning about UDL by offering a similar UDL academy.

Most faculty were able to incorporate UDL elements into their courses and responded positively to the concept of integrating UDL in the classroom, feeling that it helped improve their teaching. These results demonstrate that faculty can integrate UDL into higher education to use effective teaching strategies after participating in a UDL academy.

This paper is an original work describing a campus UDL initiative from a librarian's perspective.

The qualitative approach was applied the discover the optimum answers to the research objectives, which are (1) to understand the cultural and hedonistic characteristics of the (Lanna) Songkran festival; and (2) to examine the dilemma between cultural rituals and hedonistic activity for tourism.

This study used a case study of the Songkran festival in Chiang Mai to examine the dilemma between cultural rituals and hedonism for tourism, which brings lost or misperceived cultural values and identities. The semi-structured interview (SSI) with senior locals and participant observation during the festival was conducted in Chiang Mai, Thailand, to obtain the in-depth phenomena of the existing celebration pattern at the festival.

The study findings show three crucial phenomena that explain characteristics of unsynchronized cultural rituals and hedonistic activities for tourism: (1) the parallel phenomenon between cultural values and celebration practice, (2) the movement of local culture and(3) the hedonistic characteristics of the festival.

The study extends the knowledge on the interplay phenomena between cultural festivals and tourism; also, the involved stakeholders, such as local communities, public sectors and private sectors, can use the study findings in creating policies for using cultural festivals to promote a destination and urban economic development that will minimise cultural values distort while increase tourism economic values.

This study was conducted qualitatively, including SSIs and participant observation at the Songkran festival in Chiang Mai. The study findings were analysed, based on the empirical data, into significant themes representing the characteristics of dilemma phenomena within the festival.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.