Item Response Theory for Peer Assessment

As an assessment method based on a constructivist approach, peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. Those models are expected to improve the reliability if the model parameters can be estimated accurately. However, when applying them to actual peer assessment, the parameter estimation accuracy would be reduced for the following reasons. 1) The number of rater parameters increases with two or more times the number of raters because the models include higher-dimensional rater parameters. 2) The accuracy of parameter estimation from sparse peer assessment data depends strongly on hand-tuning parameters, called hyperparameters. To solve these problems, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible. Furthermore, this article presents a proposal of a parameter estimation method using a hierarchical Bayes model for the proposed model that can learn the hyperparameters from data. Finally, this article describes the effectiveness of the proposed method using results obtained from a simulation and actual data experiments

Mechanisms of the Penetration of Blood-Borne Substances into the Brain

The blood-brain barrier (BBB) impedes the influx of intravascular compounds from the blood to the brain. Few blood-borne macromolecules are transferred into the brain because vesicular transcytosis in the endothelial cells is considerably limited and the tight junction is located between the endothelial cells. At the first line of the BBB, the endothelial glycocalyx which is a negatively charged, surface coat of proteoglycans, and adsorbed plasma proteins, contributes to the vasculoprotective effects of the vessels wall and are involved in maintaining vascular permeability. In the endothelial cytoplasm of cerebral capillaries, there is an asymmetrical array of metabolic enzymes such as alkaline phosphatase, acid phosphatase, 5’-nucleotidase, adenosine triphosphatase, and nucleoside diphosphatase and these enzymes contribute to inactivation of substrates. In addition, there are several types of influx or efflux transporters at the BBB, such as P-glycoprotein (P-gp), multidrug resistance associated protein, breast cancer resistance protein, organic anion transporters, organic cation transporters, organic cation transporter novel type transporters, and monocarboxylic acid transporters. P-gp, energy-dependent efflux transporter protein, is instrumental to the barrier function. Several findings recently reported indicate that endothelial P-gp contributes to efflux of undesirable substances such as β-amyloid protein from the brain or periarterial interstitial fluid, while P-gp likely plays a crucial role in the genesis of multiple vascular abnormalities that accompany hypertension. In this review, influx and efflux mechanisms of drugs at the BBB are also reviewed and how medicines pass the BBB to reach the brain parenchyma is discussed

Empirical comparison of item response theory models with rater\u27s parameters

In various assessment contexts including entrance examinations, educational assessments, and personnel appraisal, performance assessment by raters has attracted much attention to measure higher order abilities of examinees. However, a persistent difficulty is that the ability measurement accuracy depends strongly on rater and task characteristics. To resolve this shortcoming, various item response theory (IRT) models that incorporate rater and task characteristic parameters have been proposed. However, because various models with different rater and task parameters exist, it is difficult to understand each model\u27s features. Therefore, this study presents empirical comparisons of IRT models. Specifically, after reviewing and summarizing features of existing models, we compare their performance through simulation and actual data experiments

ピアアセスメントにおける異質評価者に頑健な項目反応理論

近年，MOOCsに代表される大規模eラーニングの普及に伴い，ピアアセスメントを学習者の能力測定に用いるニーズが高まっている．一方で，ピアアセスメントによる能力測定の課題として，その測定精度が評価者の特性に強く依存する問題が指摘されてきた．この問題を解決する手法の一つとして，評価者特性パラメータを付与した項目反応モデルが近年多数提案されている．しかし，既存モデルでは，評価基準が他の評価者と極端に異なる“異質評価者”の特性を必ずしも表現できないため，異質評価者が存在する可能性があるピアアセスメントに適用したとき能力測定精度が低下する問題が残る．この問題を解決するために，本論文では，1）評価の厳しさ，2）一貫性，3）尺度範囲の制限，に対応する評価者特性パラメータを付与した新たな項目反応モデルを提案する．提案モデルの利点は次のとおりである．1）評価者の特性を柔軟に表現できるため，異質評価者の採点データに対するモデルのあてはまりを改善できる．2）異質評価者の影響を正確に能力測定値に反映できるため，異質評価者が存在するピアアセスメントにおいて，既存モデルより高精度な能力測定が期待できる．本論文では，シミュレーション実験と実データ実験から提案モデルの有効性を示す．Item response theory (IRT) model that incorporates rater characteristic parameters have recently been proposed to improve peer assessment accuracy. However, the assessment accuracy based on the models will be reduced when the number of aberrant raters increases because they can necessarily not capture those characteristics. To resolve the problem, we propose a new IRT model that incorporates rater characteristic parameters corresponding to severity, consistency, and range restriction. The proposed model has the following advantages. 1) The model fitting to aberrant raters\u27 data is expected to be improved because the proposed model can represent rater characteristics flexibly. 2) Peer assessment accuracy is expected to be improved even when aberrant raters exist because learner ability can be estimated as to reflect aberrant raters\u27 characteristics more accurately. Through simulation and actual data experiments, we demonstrate effectiveness of the proposed model

Genus and Differential of Function Fields with Stichtenoth Polynomials

㎞this　p叩er，　we　determine　the　genus　of　function　field　wiOI　a　Stichtenoth　po量ynomial　l㎞ the・first・three・sections（【KU】）．　ln　seCtion　4，　we　gi▼e　an　examp聖e　of　our　genus　f（｝mula，　an“ we　detem盛ne¢he　diVisor　of¢he　differentia量dUk

Group optimization to maximize peer assessment accuracy using item response theory and integer programming

With the wide spread of large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner\u27s assessment workload. However, in such cases, the peer assessment accuracy depends on the method of forming groups. To resolve that difficulty, this study proposes a group formation method to maximize peer assessment accuracy using item response theory and integer programming. Experimental results, however, have demonstrated that the proposed method does not present sufficiently higher accuracy than a random group formation method does. Therefore, this study further proposes an external rater assignment method that assigns a few outside-group raters to each learner after groups are formed using the proposed group formation method. Through results of simulation and actual data experiments, this study demonstrates that the proposed external rater assignment can substantially improve peer assessment accuracy

Bayes factorを用いたRAIアルゴリズムによる大規模ベイジアンネットワーク学習

漸近一致性をもつベイジアンネットワークの構造学習はNP困難である．これまで動的計画法やA*探索，整数計画法による探索アルゴリズムが開発されてきたが，未だに60ノード程度の構造学習を限界とし，大規模構造学習の実現のためには，全く異なるアプローチの開発が急務である．一方で因果モデルの研究分野では，条件付き独立性テスト（CIテスト）と方向付けによる画期的に計算量を削減した構造学習アプローチが提案されている．このアプローチは制約ベースアプローチと呼ばれ，RAIアルゴリズムが最も高精度な最先端学習法として知られている．しかしRAIアルゴリズムは，CIテストに仮説検定法または条件付き相互情報量を用いている．前者の精度は帰無仮説が正しい確率を表すp値とユーザが設定する有意水準に依存する．p値はデータ数の増加により小さい値を取り，誤って帰無仮説を棄却してしまう問題が知られている．一方で，後者の精度はしきい値の設定に強く影響する．したがって，漸近的に真の構造を学習できる保証がない．本論文では，漸近一致性を有するBayes factorを用いたCIテストをRAIアルゴリズムに組み込む．これにより，数百ノードをもつ大規模構造学習を実現する．数種類のベンチマークネットワークを用いたシミュレーション実験により，本手法の有意性を示す．A score-based learning Bayesian networks is NP-hard. On the other hands, constraint-based approach, that can dynamically relaxes the computational cost, is applicable to learning huge Bayesian network structures. The approach uses conditional independence (CI) tests based on the conditional mutual information and statistical testings. However, those CI tests have no consistency. In this paper, we propose a new constraint-based learning method that uses the CI test based on the Bayes factor, which have consistency. The proposed method combines it to the RAI algorithm, that is a state-of-the-art algorithm of the constraint-based approach. The experimental result shows our proposed method provides empirically best performance