A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research

Magana, Alejandra J.; Taleyarkhan, Manaz; Alvarado, Daniela Rivera; Kane, Michael D.; Springer, John A.; Clase, Kari

doi:10.1187/cbe.13-10-0193

Cited by 82 publications

(96 citation statements)

References 80 publications

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“…This is reflected in the tremendous accumulation of bioinformatics tools and databases to empower scientific research, compared with a relatively minor increase in the number of educational resources in bioinformatics [12, 13]. While most bioinformatics educational resources and courses were designed for the undergraduate level (for example [14]), bioinformatics educational reform is sporadic at the secondary level [15, 16]. In addition, better incorporation of bioinformatics into states’ science standards is required [17].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, rules were set for the development [42] and teaching [43–45] of bioinformatics training courses, which can also be applied at the high-school level. Nevertheless, only a fraction of high-school students are exposed to these materials, in part owing to the limited integration of bioinformatics into science curricula [17], and the major challenges involved [16, 46–48]. …”

Section: Introductionmentioning

confidence: 99%

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Making authentic science accessible—the benefits and challenges of integrating bioinformatics into a high-school science curriculum

et al. 2016

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Despite the central place held by bioinformatics in modern life sciences and related areas, it has only recently been integrated to a limited extent into high-school teaching and learning programs. Here we describe the assessment of a learning environment entitled ‘Bioinformatics in the Service of Biotechnology’. Students’ learning outcomes and attitudes toward the bioinformatics learning environment were measured by analyzing their answers to questions embedded within the activities, questionnaires, interviews and observations. Students’ difficulties and knowledge acquisition were characterized based on four categories: the required domain-specific knowledge (declarative, procedural, strategic or situational), the scientific field that each question stems from (biology, bioinformatics or their combination), the associated cognitive-process dimension (remember, understand, apply, analyze, evaluate, create) and the type of question (open-ended or multiple choice). Analysis of students’ cognitive outcomes revealed learning gains in bioinformatics and related scientific fields, as well as appropriation of the bioinformatics approach as part of the students’ scientific ‘toolbox’. For students, questions stemming from the ‘old world’ biology field and requiring declarative or strategic knowledge were harder to deal with. This stands in contrast to their teachers’ prediction. Analysis of students’ affective outcomes revealed positive attitudes toward bioinformatics and the learning environment, as well as their perception of the teacher’s role. Insights from this analysis yielded implications and recommendations for curriculum design, classroom enactment, teacher education and research. For example, we recommend teaching bioinformatics in an integrative and comprehensive manner, through an inquiry process, and linking it to the wider science curriculum.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Making authentic science accessible—the benefits and challenges of integrating bioinformatics into a high-school science curriculum

et al. 2016

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“…It involves the union of computer science, mathematics, statistics and molecular biology [5]. It is in this context that the area grows exponentially [6][7][8][9]. Studies [7,[9][10][11] indicate a growth in the supply of computational resources in support of the area.…”

Section: Introductionmentioning

confidence: 99%

“…It is in this context that the area grows exponentially [6][7][8][9]. Studies [7,[9][10][11] indicate a growth in the supply of computational resources in support of the area. Nucleic acids' research presents a series of tools, like biological databases (database issue, https://nar.oxfordjournals.org/content/44/D1.toc ) and web-based software resources (web server issue, https://academic.oup.com/nar/issue/44/W1).…”

Section: Introductionmentioning

confidence: 99%

Usability as the Key Factor to the Design of a Web Server for the CReF Protein Structure Predictor: The wCReF

et al. 2018

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Abstract:Protein structure prediction servers use various computational methods to predict the three-dimensional structure of proteins from their amino acid sequence. Predicted models are used to infer protein function and guide experimental efforts. This can contribute to solving the problem of predicting tertiary protein structures, one of the main unsolved problems in bioinformatics. The challenge is to understand the relationship between the amino acid sequence of a protein and its three-dimensional structure, which is related to the function of these macromolecules. This article is an extended version of the article wCReF: The Web Server for the Central Residue Fragment-based Method (CReF) Protein Structure Predictor, published in the 14th International Conference on Information Technology: New Generations. In the first version, we presented the wCReF, a protein structure prediction server for the central residue fragment-based method. The wCReF interface was developed with a focus on usability and user interaction. With this tool, users can enter the amino acid sequence of their target protein and obtain its approximate 3D structure without the need to install all the multitude of necessary tools. In this extended version, we present the design process of the prediction server in detail, which includes: (A) identification of user needs: aiming at understanding the features of a protein structure prediction server, the end user profiles and the commonly-performed tasks; (B) server usability inspection: in order to define wCReF's requirements and features, we have used heuristic evaluation guided by experts in both the human-computer interaction and bioinformatics domain areas, applied to the protein structure prediction servers I-TASSER, QUARK and Robetta; as a result, changes were found in all heuristics resulting in 89 usability problems; (C) software requirements document and prototype: assessment results guiding the key features that wCReF must have compiled in a software requirements document; from this step, prototyping was carried out; (D) wCReF usability analysis: a glimpse at the detection of new usability problems with end users by adapting the Ssemugabi satisfaction questionnaire; users' evaluation had 80% positive feedback; (E) finally, some specific guidelines for interface design are presented, which may contribute to the design of interactive computational resources for the field of bioinformatics. In addition to the results of the original article, we present the methodology used in wCReF's design and evaluation process (sample, procedures, evaluation tools) and the results obtained.

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“…A field specialising in application of computation to biological problems has emerged, known as bioinformatics. The development of bioinformatics is discussed by Hogeweg (2011), and university-level bioinformatics education has been reviewed by Magana et al (2014).…”

Section: Introductionmentioning

confidence: 99%

University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school

et al. 2015

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Background: Bioinformatics-the use of computers in biology-is of major and increasing importance to biological sciences and medicine. We conducted a preliminary investigation of the value of bringing practical, university-level bioinformatics education to the school level. We conducted voluntary activities for pupils at two schools in Scotland (years S5 and S6; pupils aged 15-17). We used material originally developed for an optional final-year undergraduate module and now incorporated into 4273π, a resource for teaching and learning bioinformatics on the low-cost Raspberry Pi computer.Results: Pupils' feedback forms suggested our activities were beneficial. During the course of the activity, they provide strong evidence of increase in the following: pupils' perception of the value of computers within biology; their knowledge of the Linux operating system and the Raspberry Pi; their willingness to use computers rather than phones or tablets; their ability to program a computer and their ability to analyse DNA sequences with a computer. We found no strong evidence of negative effects.Conclusions: Our preliminary study supports the feasibility of bringing university-level, practical bioinformatics activities to school pupils.

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A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research

Cited by 82 publications

References 80 publications

Making authentic science accessible—the benefits and challenges of integrating bioinformatics into a high-school science curriculum

Making authentic science accessible—the benefits and challenges of integrating bioinformatics into a high-school science curriculum

Usability as the Key Factor to the Design of a Web Server for the CReF Protein Structure Predictor: The wCReF

University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school

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