Fumiya Okubo

Preliminary Research on Self-Regulated Learning and Learning Logs in a Ubiquitus Learning Environment

This preliminary research investigates the relationship between psychometric data and learning behaviors in the learning analytics research field, specifically, the relationship between self-regulated learning and learning behavior. The results of this limited research show that marker and annotation use have a weak significant relationship with self-efficacy and the intrinsic value of learning materials.

A neural network approach for students' performance prediction

In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.

Informal Learning Behavior Analysis Using Action Logs and Slide Features in E-Textbooks

This paper discusses learning behavior analysis using a learning management system (LMS) and an e-textbook system. We collected a large number of operation logs from e-textbooks to analyze the process of learning. In addition, we conducted a quiz to check the level of understanding. In our study, we especially focus on an analysis of the relationship between learning behavior in informal learning and its effectiveness in the corresponding quiz. We apply a machine learning and classification methodology for behavior analysis. Our experimental results demonstrate that students who undertake good informal learning achieve better scores in quizzes.

The Computational Capability of Chemical Reaction Automata

We propose a new computing model called chemical reaction automata (CRAs) as a simplified variant of reaction automata (RAs) studied in recent literature ([7-9]).

The computational capability of chemical reaction automata

We propose a new computing model called chemical reaction automata (CRAs) as a simplified variant of reaction automata (RAs) studied in recent literature (Okubo in RAIRO Theor Inform Appl 48:23–38 2014; Okubo et al. in Theor Comput Sci 429:247–257 2012a, Theor Comput Sci 454:206–221 2012b). We show that CRAs in maximally parallel manner are computationally equivalent to Turing machines, while the computational power of CRAs in sequential manner coincides with that of the class of Petri nets, which is in marked contrast to the result that RAs (in both maximally parallel and sequential manners) have the computing power of Turing universality (Okubo 2014; Okubo et al. 2012a). Intuitively, CRAs are defined as RAs without inhibitor functioning in each reaction, providing an offline model of computing by chemical reaction networks (CRNs). Thus, the main results in this paper not only strengthen the previous result on Turing computability of RAs but also clarify the computing powers of inhibitors in RA computation.

Recent Developments on Reaction Automata Theory: A Survey

This paper surveys recent developments on the theory of reaction automata, which has been lately initiated in [17] to model and analyze in the computational framework the behaviors of biochemical reactions in nature. Reaction automata (RAs) have been proposed as computing models for accepting string languages. RAs may be taken as a kind of an extension of reaction systems in that they deal with multisets rather than (usual) sets being dealt with in reaction systems. A computation process by an RA is performed in such a way that after taking in the system an input symbol from the environment, the RA changes its state (represented by a multiset) by applying reaction rules to the multiset in the manner designated, where the maximally parallel manner is considered as well as the (usual) sequential manner. An input sequence of symbols is accepted if the RA stays in a final state (i.e., a designated multiset) at some moment after reading through the input. Thus, RAs may also be regarded as a variant of finite automata in which multisets are used to play a role of (unbounded number of) states. The presented results are all from [16, 17, 18] and include: RAs have the Turing universal computation power, the computation power of exponential-bounded RAs coincides with that of the linear-bounded Turing machines, the computation power of linear-bounded RAs is incomparable to that of pushdown automata. Further, the case for RAs with sequential mode of rule applications is also investigated.

The Computing Power of Determinism and Reversibility in Chemical Reaction Automata

Chemical reaction automata (CRAs) are computing models with multiset storage based on multiset rewriting introduced in Okubo, Yokomori, (DNA20, LNCS, vol. 8727, pp. 53–66, (2014), [25]). A CRA consists of a finite set of reactions (or pairs of multisets called reactants and products, respectively) and an initial multiset as well as a set of final multisets. Taking an input symbol in the current configuration (multiset) a CRA changes it into a new configuration. Thus, a CRA offers an automaton-like computing model to investigate the computational analysis of chemical reactions. On the other hand, since any (irreversible) Turing machine was proven to be effectively simulated by a reversible Turing machine in Bennett, (IBM J Res Dev, 17(6), 525–532, (1973), [4]), reversible computing has become a research field that has been receiving increased attention. In this paper we introduce the notions of determinism and reversibility into CRAs, and investigate the computational powers of those classes of CRAs in comparison with the language classes of Chomsky hierarchy. The computing power of reversible CRAs involves the physical realization of molecular programming of chemical reaction networks (Thachuk, Condon, DNA 18, LNCS, vol. 7433, pp. 135–149, (2012), [32]) with DNA strand displacement system implementation (Qian, Winfree, Science, 332, 1196–1201, (2011), [29]), and therefore, it is of great significance to elucidate the computing capabilities of both deterministic and reversible CRAs from the theoretical viewpoint of molecular computing.

Automatic Summarization of Lecture Slides for Enhanced Student Preview–Technical Report and User Study–

This paper is an extension of research originally reported in [1] . Here, we propose a novel method for summarizing lecture slides to enhance students’ preview efficiency and understanding of the content. Students are often asked to prepare for a class by reading lecture materials. However, because the attention span of students is limited, this is not always beneficial. We surveyed 326 students regarding the preview of lecture materials, revealing a preference for summarized materials to preview. Therefore, we developed an automatic summarization method for condensing original lecture materials into a summarized set. Our proposed approach utilizes image and text processing to extract important pages from lecture materials, optimizing selection of pages in accordance with a specified preview time. We applied the proposed summarization method to a set of lecture slides. In an experiment with 372 students, we compared the effectiveness of the summarized slides and the original materials in terms of quiz scores, preview achievement ratio, and time spent previewing. We found that students who previewed the summarized slides achieved better scores on pre-lecture quizzes, even though they spent less time previewing the material.

Reproducibility of findings from educational big data: a preliminary study

In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.

Finite Automata with Multiset Memory: A New Characterization of Chomsky Hierarchy

This paper concerns new characterizations of language classes in the Chomsky hierarchy in terms of a new type of computing device called FAMM Finite Automaton with Multiset Memory in which a multiset of symbol objects is available for the storage of working space. Unlike the stack or the tape for a storage, the multiset might seem to be less powerful in computing task, due to the lack of positional structural information of stored data. We introduce the class of FAMMs of degree d for non-negative integer d in general form, and investigate the computing powers of some subclasses of those FAMMs. We show that the classes of languages accepted by FAMMs of degree 0, by FAMMs of degree 1, by exponentially-bounded FAMMs of degree 2, and by FAMMs of degree 2 are exactly the four classes of languages REG, CF, CS and RE in the Chomsky hierarchy, respectively. Thus, this unified view from multiset-based computing provides new insight into the computational aspects of the Chomsky hierarchy.