hierarchical knowledge

Purpose – Due to its technical focus, the introductory accounting course has a hierarchical knowledge
structure that requires students to master and integrate abstract knowledge which builds on itself over time.
The purpose of this paper is to explore the relationship between engagement and examination performance
for students enrolled in a hierarchically structured course.
Design/methodology/approach – This research involves a retrospective study of an introduction to
accounting course examining the relationship between increased engagement and examination performance.
Students are provided opportunities for engagement through assigned homework and optional ungraded
assignments. Performance is measured by scores on each of three examinations conducted throughout
the semester.
Findings – The study finds that additional engagement in assignments has no significant impact on
mid-semester examination performance; however, sustained engagement throughout the semester has a
cumulative impact on final examination performance. Moreover, students that perform well on mid-semester
examinations do not benefit from additional engagement, whereas students that perform poorly on the
mid-semester examinations exhibit substantially higher final examination scores from sustained engagement.
Practical implications – This study illustrates the complex interplay between engagement and
performance and the timing of performance gains. The implication for educators is that increased sustained
engagement is likely to result in increased but delayed student performance gains in disciplines with
hierarchical knowledge structures.
Originality/value – This study contributes to the literature in its examination of the timing of performance
benefits gained from increased engagement in courses with a cumulative knowledge base.

Considering relational tables as the object of analysis, methods to summarize them can help the analyst to have a starting point to explore the data. Typically, table summarization aims at producing an informative data summary through the use of metadata supplied by attribute taxonomies. Nevertheless, such a hierarchical knowledge is not always available or may even be inadequate when existing. To overcome these limitations, we propose a new framework, named cTabSum, to automatically generate attribute value taxonomies and directly perform table summarization based on its own content. Our innovative approach considers a relational table as input and proceeds in a two-step way. First, a taxonomy for each attribute is extracted. Second, a new table summarization algorithm exploits the automatic generated taxonomies. An information theory measure is used to guide the summarization process. Associated with the new algorithm we also develop a prototype. Interestingly, our prototype incorporates some additional features to help the user familiarizing with the data: (i) the resulting summarized table produced by cTabSum can be used as recommended starting point to browse the data; (ii) some very easy-to-understand charts allow to visualize how taxonomies have been so built; (iii) finally, standard OLAP operators, i.e. drill-down and roll-up, have been implemented to easily navigate within the data set. In addition we also supply an objective evaluation of our table summarization strategy over real data.

TacAir-Soar is an intelligent, rule-based system that generates believable human-like" behavior for military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the United States military ies in xed-wing aircraft. It accomplishes this by i n tegrating a wide variety o f i n telligent capabilities, including reasoning about interacting goals, reacting to rapid changes in real time or faster, communicating and coordinating with other agents including humans, maintaining situational awareness, and accepting new orders while in ight. The system is currently deployed at the Oceana Naval Air Station WIS-SARD, and its most dramatic use to date was in the Synthetic Theater Of War 1997, an operational training exercise consisting of 48 straight hours and approximately 700 xed-wing aircraft ights, all own by instances of the TacAir-Soar system.

After reviewing the relevant theory on chess expertise, this paper reexamines experimentally the finding of Chase and Simon (1973a) that the differences in ability of chess players at different skill levels to copy and to recall positions are attributable to the experts' storage of thousands of chunks (patterned clusters of pieces) in long-term memory. Despite important differences in the experimental apparatus, the data of the present experiments regarding latencies and chess relations between successively placed pieces are highly correlated with those of Chase and Simon. We conclude that the 2second inter-chunk interval used to define chunk boundaries is robust, and that chunks have psychological reality. We discuss the possible reasons why Masters in our new study used substantially larger chunks than the Master of the 1973 study, and extend the chunking theory to take account of the evidence for large retrieval structures (templates) in long-term memory.

The article presents a hierarchical fuzzy rule base for intelligent support of decision making about cause of structural crack of stone building. According to civil engineering practice the causes of structural cracks are classified by the followings diagnoses: static overload; dynamic overload; especial overload; defects of basis and foundation; temperature influence; breach of technological process during the building. Source information needed for decision making is the data of visual investigation of building, icluding simple measuremnts. For decision making we take into account 42 input attributes. The hierarchical system ties 9 fuzzy knowledge bases, which contain 151 rules in total. Cause detection of the crack is carrying out by max-min fuzzy inference with hierarchical knowledge base. Learning of fuzzy rules by genetic algorithms provided a good matching between real causes of cracks and modeling results.

There are many complex, well structured problems, where a hierarchical structure within the data is present. This means, that one or several components of the structure are determined at a higher level by a sub-tree of other components. The concept of fuzzy signatures was introduced to help model these kinds of problems. The data set belonging to a problem has an arbitrary structure, but due to some missing components, the structures of the data may slightly differ. So that these data can be evaluated, aggregation operators are given for each node in the arbitrary structure for the purpose of modifying the structure. To deduce a conclusion for an observation from a data set having the structure mentioned above, fuzzy signature based rule bases and the generalisation of Mamdani-type inference were introduced. In this paper the formerly introduced idea of Mamdani-type inference in fuzzy signature based rule bases will be used for selecting records from an available data base which maximally match the requirements specified in a pattern. Finally a possible application on a realistic example with missing data components will be shown.

The purpose of this research is to develop effective machine learning or data mining techniques based on flexible neural tree FNT. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA).