Undergraduate engineering students\u27 experiences of interdisciplinary learning: a phenomenographic perspective

Engineers are expected to work with people with different disciplinary knowledge to solve real-world problems that are inherently complex, which is one of the reasons that interdisciplinary learning has become a common pedagogical practice in engineering education. However, empirical evidence on the impact of interdisciplinary learning on undergraduates is lacking. Regardless of the differences in the scope of methods used to assess interdisciplinary learning, frameworks of interdisciplinary learning are imperative for developing attainable outcomes as well as interpreting assessment data. Existing models of interdisciplinary learning have been either conceptual or based on research faculty members\u27 experiences rather than empirical data. The study addressed the gap by exploring the different ways that undergraduate engineering students experience interdisciplinary learning. A phenomenographic methodological framework was used to guide the design, data collection, and data analysis of the study. Twenty-two undergraduate engineering students with various interdisciplinary learning experiences were interviewed using semi-structured protocols. They concretely described their experiences and reflected meaning associated with those experiences. Analysis of the data revealed eight qualitatively different ways that students experience interdisciplinary learning, which include: interdisciplinary learning as (A) no awareness of differences, (B) control and assertion, (C) coping with differences, (D) navigating creative differences, (E) learning from differences, (F) bridging differences, (G) expanding intellectual boundaries, and (H) commitment to holistic perspectives. Categories D through H represent a hierarchical structure of increasingly comprehensive way of experiencing interdisciplinary learning. Further analysis uncovered two themes that varied throughout the categories: (i) engagement with differences and (ii) purpose and integration. Students whose experiences lie outside of the hierarchical structure need to engage difference in a positive manner and also have a purpose in engaging differences in order to experience interdisciplinary learning in a more comprehensive way. The results offer insights into the design of curriculum and classroom interdisciplinary experiences in engineering education

Flow Experience and Challenge-Skill Balance in E-Learning

Flow is an optimal experience resulting in intense engagement in the activity. People achieved flow state when they perceived balance between challenge of the activity and their skill to the activity. The concept of flow can be used to explore students’ learning performance in e-learning environment. The current research aims to empirically explore the influence of challenge-skill balance on the flow experience and the influence of flow experience on learning satisfaction and learning performance in e-learning environment. The current research conducted a quasi-experimental design with questionnaire survey and carried out an electroencephalography (EEG) analysis, a psychophysiological method. The empirical survey results have shown that challenge-skill balance is an antecedent factor affecting learners’ flow experience. Once learners reach flow experience, their learning performance and learning satisfaction would get improved. Besides, the current research also found that flow experience is relative with learners’ attention measured by EEG brainwave signal. Learners’ perception of challenge-skill balance would influence their attention in e- learning activities. The current research is also in the pioneering position that using non-medical purpose EEG device in e-learning research

MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein–protein interactions directly from sequences. The web service is available at and a mirror site at

iPDA: integrated protein disorder analyzer

This article presents a web server iPDA, which aims at identifying the disordered regions of a query protein. Automatic prediction of disordered regions from protein sequences is an important problem in the study of structural biology. The proposed classifier DisPSSMP2 is different from several existing disorder predictors by its employment of position-specific scoring matrices with respect to physicochemical properties (PSSMP), where the physicochemical properties adopted here especially take the disorder propensity of amino acids into account. The web server iPDA integrates DisPSSMP2 with several other sequence predictors in order to investigate the functional role of the detected disordered region. The predicted information includes sequence conservation, secondary structure, sequence complexity and hydrophobic clusters. According to the proportion of the secondary structure elements predicted, iPDA dynamically adjusts the cutting threshold of determining protein disorder. Furthermore, a pattern mining package for detecting sequence conservation is embedded in iPDA for discovering potential binding regions of the query protein, which is really helpful to uncovering the relationship between protein function and its primary sequence. The web service is available at http://biominer.bime.ntu.edu.tw/ipda and mirrored at http://biominer.cse.yzu.edu.tw/ipda

WildSpan: mining structured motifs from protein sequences

<p>Abstract</p> <p>Background</p> <p>Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards) are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.</p> <p>Results</p> <p>WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode of WildSpan is developed for discovering functional regions of a single protein by referring to a set of related sequences (e.g. its homologues). The discovered W-patterns are used to characterize the protein sequence and the results are compared with the conserved positions identified by multiple sequence alignment (MSA). The family-based mining mode of WildSpan is developed for extracting sequence signatures for a group of related proteins (e.g. a protein family) for protein function classification. In this situation, the discovered W-patterns are compared with PROSITE patterns as well as the patterns generated by three existing methods performing the similar task. Finally, analysis on execution time of running WildSpan reveals that the proposed pruning strategy is effective in improving the scalability of the proposed algorithm.</p> <p>Conclusions</p> <p>The mining results conducted in this study reveal that WildSpan is efficient and effective in discovering functional signatures of proteins directly from sequences. The proposed pruning strategy is effective in improving the scalability of WildSpan. It is demonstrated in this study that the W-patterns discovered by WildSpan provides useful information in characterizing protein sequences. The WildSpan executable and open source codes are available on the web (<url>http://biominer.csie.cyu.edu.tw/wildspan</url>).</p