From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education

Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students’ learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students’ self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students’ learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students’ text marking skill and facilitating precision education

Learning to Dispatch Multi-Server Jobs in Bipartite Graphs with Unknown Service Rates

Multi-server jobs are imperative in modern cloud computing systems. A multi-server job has multiple components and requests multiple servers for being served. How to allocate restricted computing devices to jobs is a topic of great concern, which leads to the job scheduling and load balancing algorithms thriving. However, current job dispatching algorithms require the service rates to be changeless and knowable, which is difficult to realize in production systems. Besides, for multi-server jobs, the dispatching decision for each job component follows the All-or-Nothing property under service locality constraints and resource capacity limits, which is not well supported by mainstream algorithms. In this paper, we propose a dispatching algorithm for multi-server jobs that learns the unknown service rates and simultaneously maximizes the expected Accumulative Social Welfare (Asw). We formulate the Asw as the sum of utilities of jobs and servers achieved over each time slot. The utility of a job is proportional to the valuation for being served, which is mainly impacted by the fluctuating but unknown service rates. We maximize the Asw without knowing the exact valuations, but approximate them with exploration-exploitation. From this, we bring in several evolving statistics and maximize the statistical Asw with dynamic programming. The proposed algorithm is proved to have a polynomial complexity and a State-of-the-Art regret. We validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively

Low energy laser light (632.8 nm) suppresses amyloid-β peptide-induced oxidative and inflammatory responses in astrocytes

Oxidative stress and inflammation are important processes in the progression of Alzheimer's disease (AD). Recent studies have implicated the role of amyloid β-peptides (Aβ) in mediating these processes. In astrocytes, oligomeric Aβ induces the assembly of NADPH oxidase complexes resulting in its activation to produce anionic superoxide. Aβ also promotes production of pro-inflammatory factors in astrocytes. Since low energy laser has previously been reported to attenuate oxidative stress and inflammation in biological systems, the objective of this study was to examine whether this type of laser light was able to abrogate the oxidative and inflammatory responses induced by Aβ. Primary rat astrocytes were exposed to Helium-Neon laser (λ=632.8 nm), followed by the treatment with oligomeric Aβ. Primary rat astrocytes were used to measure Aβ-induced production of superoxide anions using fluorescence microscopy of dihydroethidium (DHE), assembly of NADPH oxidase subunits by the colocalization between the cytosolic p47phox subunit and the membrane gp91phox subunit using fluorescent confocal microscopy, phosphorylation of cytosolic phospholipase A2 (cPLA2), and expressions of pro-inflammatory factors including interleukin-1β (IL-1β) and inducible nitric-oxide synthase (iNOS) using Western blot Analysis. Our data showed that laser light at 632.8 nm suppressed Aβ-induced superoxide production, colocalization between NADPH oxidase gp91phox and p47phox subunits, phosphorylation of cPLA2, and the expressions of IL-1β and iNOS in primary astrocytes. We demonstrated for the first time that 632.8 nm laser was capable of suppressing cellular pathways of oxidative stress and inflammatory responses critical in the pathogenesis in AD. This study should prove to provide the groundwork for further investigations for the potential use of laser therapy as a treatment for AD