The Effects of the Orff Approach on Language Acquisition for Spanish Foreign Language Students

Despite the abundance of literature that supports music education connecting to language learning, limited research evaluates the effectiveness of elementary music methodologies, such as the Orff approach, in helping foreign language students in their language learning. The Orff approach develops musicianship in every student through music, movement, speech, and drama. Guided by Gardner’s theory of multiple intelligences, the researcher implemented a quasi-experimental research study to measure the language fluency of 100 elementary students participating in general music and learning Spanish as a foreign language. The researcher placed half of the students in the treatment group exploring the Orff approach in general music and half in the control group in music appreciation. After completing a Spanish pretest and participating in the two-month intervention, both groups are assessed via the Spanish Student Growth Objective (SGO) halfway benchmark. Scores reflect students’ listening, speaking, reading, and writing abilities. This work provides evidence of the effects of the Orff approach on language acquisition. It allows readers to ascertain the potential connections between the brain regions responsible for language learning and those responsible for developing musicianship. Such a study is groundbreaking because it can inspire the development of professional learning communities among the arts and language departments and promote further cross-curricular connections to music. Furthermore, this study can encourage further research as scholars can test various general music methodologies and successful acquisition of other target or foreign languages

TANDEM: taming failures in next-generation datacenters with emerging memory

The explosive growth of online services, leading to unforeseen scales, has made modern datacenters highly prone to failures. Taming these failures hinges on fast and correct recovery, minimizing service interruptions. Applications, owing to recovery, entail additional measures to maintain a recoverable state of data and computation logic during their failure-free execution. However, these precautionary measures have severe implications on performance, correctness, and programmability, making recovery incredibly challenging to realize in practice. Emerging memory, particularly non-volatile memory (NVM) and disaggregated memory (DM), offers a promising opportunity to achieve fast recovery with maximum performance. However, incorporating these technologies into datacenter architecture presents significant challenges; Their distinct architectural attributes, differing significantly from traditional memory devices, introduce new semantic challenges for implementing recovery, complicating correctness and programmability. Can emerging memory enable fast, performant, and correct recovery in the datacenter? This thesis aims to answer this question while addressing the associated challenges. When architecting datacenters with emerging memory, system architects face four key challenges: (1) how to guarantee correct semantics; (2) how to efficiently enforce correctness with optimal performance; (3) how to validate end-to-end correctness including recovery; and (4) how to preserve programmer productivity (Programmability). This thesis aims to address these challenges through the following approaches: (a) defining precise consistency models that formally specify correct end-to-end semantics in the presence of failures (consistency models also play a crucial role in programmability); (b) developing new low-level mechanisms to efficiently enforce the prescribed models given the capabilities of emerging memory; and (c) creating robust testing frameworks to validate end-to-end correctness and recovery. We start our exploration with non-volatile memory (NVM), which offers fast persistence capabilities directly accessible through the processor’s load-store (memory) interface. Notably, these capabilities can be leveraged to enable fast recovery for Log-Free Data Structures (LFDs) while maximizing performance. However, due to the complexity of modern cache hierarchies, data hardly persist in any specific order, jeop- ardizing recovery and correctness. Therefore, recovery needs primitives that explicitly control the order of updates to NVM (known as persistency models). We outline the precise specification of a novel persistency model – Release Persistency (RP) – that provides a consistency guarantee for LFDs on what remains in non-volatile memory upon failure. To efficiently enforce RP, we propose a novel microarchitecture mechanism, lazy release persistence (LRP). Using standard LFDs benchmarks, we show that LRP achieves fast recovery while incurring minimal overhead on performance. We continue our discussion with memory disaggregation which decouples memory from traditional monolithic servers, offering a promising pathway for achieving very high availability in replicated in-memory data stores. Achieving such availability hinges on transaction protocols that can efficiently handle recovery in this setting, where compute and memory are independent. However, there is a challenge: disaggregated memory (DM) fails to work with RPC-style protocols, mandating one-sided transaction protocols. Exacerbating the problem, one-sided transactions expose critical low-level ordering to architects, posing a threat to correctness. We present a highly available transaction protocol, Pandora, that is specifically designed to achieve fast recovery in disaggregated key-value stores (DKVSes). Pandora is the first one-sided transactional protocol that ensures correct, non-blocking, and fast recovery in DKVS. Our experimental implementation artifacts demonstrate that Pandora achieves fast recovery and high availability while causing minimal disruption to services. Finally, we introduce a novel target litmus-testing framework – DART – to validate the end-to-end correctness of transactional protocols with recovery. Using DART’s target testing capabilities, we have found several critical bugs in Pandora, highlighting the need for robust end-to-end testing methods in the design loop to iteratively fix correctness bugs. Crucially, DART is lightweight and black-box, thereby eliminating any intervention from the programmers

MULTIPAC, a multiple pool processor and computer for a spacecraft central data system, phase 2 Final report

MULTIPAC, multiple pool processor and computer for deep space probe central data syste

March 20, 1975

https://scholarlycommons.obu.edu/arbn_75-79/1011/thumbnail.jp

Taylor: A Magazine for Taylor University Alumni and Friends (Winter 1997)

The Winter 1997 edition of Taylor Magazine, published by Taylor University in Upland, Indiana.https://pillars.taylor.edu/tu_magazines/1156/thumbnail.jp

The Montclarion, January 30, 1997

Student Newspaper of Montclair State Universityhttps://digitalcommons.montclair.edu/montclarion/1775/thumbnail.jp