Readers will find several papers that address high-level issues in the use of technology in education, for example architecture and design frameworks for building online education materials or tools. Several other chapters report novel approaches to intelligent tutors or adaptive systems in educational settings. A number of chapters consider many roles for social computing in education, from simple computer-mediated communication support to more extensive community-building frameworks and tools. Finally, several chapters report state-of-the-art results in tools that can be used to assist educators in critical tasks such as content presentation and grading.

Mercaptoundecahydrododecaborate (BSH)-encapsulating 10% distearoyl boron lipid (DSBL) liposomes were developed as a boron delivery vehicle for neutron capture therapy. The current approach is unique because the liposome shell itself possesses cytocidal potential in addition to its encapsulated agents. BSH-encapsulating 10% DSBL liposomes have high boron content (B/P ratio: 2.6) that enables us to prepare liposome solution with 5000 ppm boron concentration. BSH-encapsulating 10% DSBL liposomes displayed excellent boron delivery efficacy to tumor: boron concentrations reached 174, 93, and 32 ppm at doses of 50, 30, and 15 mg B/kg, respectively. Magnescope was also encapsulated in the 10% DSBL liposomes and the real-time biodistribution of the Magnescope-encapsulating DSBL liposomes was measured in a living body using MRI. Significant antitumor effect was observed in mice injected with BSH-encapsulating 10% DSBL liposomes even at the dose of 15 mg B/kg; the tumor completely disappeared three weeks after thermal neutron irradiation ((1.5-1.8) × 10(12) neutrons/cm(2)). The current results enabled us to reduce the total dose of liposomes to less than one-fifth compared with that of the BSH-encapsulating liposomes without reducing the efficacy of boron neutron capture therapy (BNCT).

We succeeded in the synthesis of the double-tailed boron cluster lipids 4a-c and 5a-c, which have a B12H11S moiety as a hydrophilic function, by S-alkylation of B12H11SH (BSH) with bromoacetyl and chloroacetocarbamate derivatives of diacylglycerols for a liposomal boron delivery system on neutron capture therapy. Calcein encapsulation experiments revealed that the liposomes, prepared from the boron cluster lipid 4b, DMPC, PEG-DSPE, and cholesterol, are stable at 37 degrees C in FBS solution for 24 h. [reaction: see text].

No abstract

No abstract

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's 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.

The fluorescence-labeled closo-dodecaborane lipid (FL-SBL) was synthesized from (S)-(+)-1,2-isopropylideneglycerol as a chiral starting material. FL-SBL was readily accumulated into the PEGylated DSPC liposomes prepared from DSPC, CH, and DSPE-PEG-OMe by the post insertion protocol. The boron concentrations and the fluorescent intensities of the FL-SBL-labeled DSPC liposomes increased with the increase of the additive FL-SBL, and the maximum emission wavelength of the liposomes appeared at 531 nm. A preliminary in vivo imaging study of tumor-bearing mice revealed that the FL-SBL-labeled DSPC liposomes were delivered to the tumor tissue but not distributed to hypoxic regions.

Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by human raters. Conventional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks (DNNs) to obviate the need for feature engineering. Furthermore, hybrid methods that integrate handcrafted features in a DNN-AES model have been recently developed and have achieved state-of-the-art accuracy. One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentencelevel features. However, this method has the following problems: 1) It cannot incorporate effective essay-level features developed in previous AES research. 2) It greatly increases the numbers of model parameters and tuning parameters, increasing the difficulty of model training. 3) It has an additional RNN to process sentence-level features, enabling extension to various DNN-AES models complex. To resolve these problems, we propose a new hybrid method that integrates handcrafted essay-level features into a DNN-AES model. Specifically, our method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model. Our method is a simple DNN-AES extension, but significantly improves scoring accuracy.