Teaching process simulation using video-enhanced and discovery/inquiry-based learning: methodology and analysis within a theoretical framework for skill acquisition

Daniel J. Belton

School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK

d.j.belton@hud.ac.uk

Abstract

Process simulation has become an essential tool for chemical engineers in education and industry. Various studies examining the teaching and learning of process simulation are available, although no clear theoretical frameworks for process simulation pedagogy currently exist. The work presented here describes a methodology for teaching process simulation that utilises video-enhanced and exploratory-based learning. The teaching approach is evaluated for a cohort of first year students, with the evaluation drawing on tutor observations, online survey responses and interviews with students. These data sources are used to explore the student experience and reveal that students engaged positively with the learning process. They also show that students benefitted from and valued the learning approaches used. Furthermore, interview responses were interrogated in detail using a thematic analysis, which revealed several key themes. The learning process is observed to occur in distinct phases, with each phase being underpinned by different learning modalities. An ‘early’ phase of learning is identified, which is supported by expository learning, whereas a ‘late’ phase of learning, also identified, is supported by a combination of discovery- and inquiry-based learning. A possible ‘future’ phase of learning is also described, where it is anticipated students could develop their process simulation skills further. These phases of learning are noted and observed to be linked with various stages of skill acquisition and cognition. The learning process is also supported by a range of factors, including student meta-cognition, motivation and knowledge development but hindered by a number of potential obstacles. Overall, the findings, supported by student quotations, provide a rich picture of how students can progress through successive levels of skill development in process simulation, forming a proposed learning model for process simulation pedagogy.

Keywords: process simulation, skill acquisition, active learning, inquiry-based learning, technology-enhanced learning, qualitative research

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1. Introduction

Process simulation has become a ubiquitous and indispensable tool in chemical engineering (Stephanopoulos and Reklaitis, 2011). As such, the importance of process simulation and related computing skills for employability of chemical engineering graduates is widely reported (Grant and Dickson, 2006; Lewin et al., 2002; Ng and Chong, 2013; Tyson, 2013). In line with this, the essential role of process simulation in chemical engineering education has also been acknowledged (Dahm et al., 2002; Ng and Chong, 2013; Silverstein, 2004). Whilst the literature review by Dahm et al. (2002) concludes that process simulation is an important part of the chemical engineering curriculum, they suggest that it is sometimes underused in university programmes. They also point out that process simulation should not be taught to the exclusion of other industrially relevant software tools. One potential barrier highlighted for holding back coherent teaching of process simulation is the unwillingness of faculty members to learn how to use new and complicated pieces of software. The overriding theme that emerges is that process simulation should not just be introduced and used for process design in the final year of degree programmes but rather that it should be introduced from year one and expanded into the wider curriculum. Overall, it would appear that integrated and scaffolded approaches might be effective in achieving this goal.

Various practitioner case studies for teaching process simulation have been reported in the literature (Dahm, 2002; Komulainen et al., 2012; Lewin et al., 2006; Ng and Chong, 2013; Silverstein, 2004; Wankat, 2002), but no clear theoretical frameworks or evaluation strategies have emerged for process simulation pedagogy. Ng and Chong (2013) provide a narrative account for the set up and implementation of process simulation teaching across the curriculum and at all levels of the degree programme. Whilst the teaching model is linked to educational theory, there is no evaluation of its effectiveness and the learner perspective is not represented. Lewin et al. (2006) also describe an integrated approach to the set up and delivery of process simulation teaching and include quantitative data on the student perspective, providing some useful insights. Lakshmanan et al. (2012) also advocate a curriculum based approach to teaching process simulation. In addition, they suggest that a multimedia approach can enhance student learning. This appears to be based on the work of Lewin et al. (2002), who indicate that multimedia delivery of teaching allows students to take a self-paced approach to developing mastery of process simulation. Online learning resources to support such an approach include tutorials with step-by-step instructions, screenshots, audio podcasts, screencasts and animations (Seider et al., 2010).

Whilst engineering education research is an active area (Aung et al., 2004; Borrego and Bernhard, 2011; Jesiek et al., 2010; Jesiek et al., 2009; Smith, 1991), the evaluation element of this work is a potential area of weakness, since it is sometimes absent or when included it often focuses on quantitative data or is simply based on subjective “feelings” as to the value of a particular teaching approach (Dutson et al., 1997). Although the use of quantitative analysis is very effective for showing what is happening, it does not elucidate how and why something is happening (Denzin and Lincoln, 2011). In order to achieve this, a qualitative research approach is required. Understanding why a particular phenomenon is occurring can be extremely powerful, especially in education research investigations and dissemination. Such understanding can influence teacher and student approaches to thinking, learning and skill development, by facilitating meta-cognitive development and by encouraging reflective practice (Case and Gunstone, 2002; Mann et al., 2009; Ramey Gassert et al., ‐1996; Schraw et al., 2006).

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Generalised theoretical frameworks exist for the acquisition and development of new skills, with potential relevance and possible implications for teaching process simulation. Whilst skill acquisition has been extensively studied from a cognitive science perspective (Johnson et al., 2006; Salvucci, 2013; Scott and Bansal, 2013; Speelman and Kirsner, 2005; VanLehn, 1996), a qualitative understanding of skill acquisition is more pertinent to the present work. For example, the Dreyfus and Dreyfus five stage model of skill acquisition was originally introduced in 1980 to understand skill development of aircraft pilots but has since been further developed (Dreyfus et al., 1986; Dreyfus, 2004; Dreyfus and Dreyfus, 1980). It has also been reapplied and reimagined for other fields, including nursing and software development (Benner, 2001; Hunt, 2008). The model suggests that someone can develop new skills by passing through five stages of development, from having no prior experience as a ‘novice’ through to becoming an ‘expert’ via the ‘advanced beginner’, ‘competent’ and ‘proficient’ stages of development. A conceptual understanding of how someone might think at the various stages of development has important implications for how they should be guided and instructed. For example, there are several important and informative distinctions between novices and experts. Notably, novices rely on rules whilst experts rely on experience and sophisticated pattern matching. Novices see a problem as a collection of equally relevant parts whilst experts see problems as a complete and unique whole where only certain elements are important. Complementary to this model is the idea that skills can be developed through ‘deliberate practice’ (Ericsson, 2008). This involves working on a well-defined task. The task needs to be appropriately difficult (challenging but doable). The learning environment needs to be informative, providing feedback that can be acted upon. The learning environment also needs to provide opportunities for repetition. This allows skills and expertise to be reinforced and for any actions, corrected by feedback, to be retried and tested. Such qualitative models can aid an instructor by providing insights into the thought processes and difficulties experienced by students, allowing the teaching experience to be designed and augmented to meet learner needs.

The present work examines the teaching of steady-state process simulation to first year chemical engineering students using screencast videos and exploratory-based learning. The aim of the work is to examine how student learning happens during this teaching and to situate observations made in the context of existing pedagogic theory. The teaching and learning process is evaluated using tutor observations, online survey responses, student interviews and a qualitative thematic analysis approach.

2. Methodology

2.1 Teaching methodology

Process simulation was taught by the author to 36 chemical engineering students on a module taught within the first year of BEng Chemical Engineering and BSc Chemical Engineering and Chemistry pathways. This process simulation training constituted one fifth of a 10 ECTS credit chemical engineering design module. Contact time was split across six sessions for two hours per fortnight in a PC lab during the second half of the year. The software used was SIMSCI PRO/II 9.2 (Schneider Electric, formerly Invensys), steady-state process simulator. The learning was supported via twenty four instructional videos, watched by the students during the class time and hosted by the university’s closed access video streaming website: https://unitube.hud.ac.uk.1 The videos demonstrate how to build and run a simulation and how to use various features of the software.

1 These videos are now freely available at www.youtube.com/c/ChemEngTutor.

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These were made by capturing an audio commentary along what was being shown on the tutors computer screen as each demonstration of the software was carried out. This approach to video capture is commonly referred to as a ‘screencasting’. The use of screencasts to support the development of competence in using software is well-documented and has been shown to be particularly effective for facilitating basic and intermediary levels of skill acquisition (Ali et al., 2011; DeVaney, 2009; Hardin and Ellington, 2005; Lang and Ceccucci, 2014; Lee et al., 2008; van der Meij and van der Meij, 2015; Veronikas and Maushak, 2005).

The teaching and learning was structured around a number of coursework tasks that had to be completed in sequence during the class time, with each new task designed to build on the previous with the tutor on-hand to provide guidance as needed. The first two tasks utilised an iterative learning cycle, whereby students would watch a number of instructional videos, replicate what was shown, test their simulation against the result shown in the video and then go back over their simulation and the videos if the correct result was not observed. Once the correct result had been obtained, students were required to upload the file of their completed simulation, in order to evidence the completion of the work and to preserve a record of the work for plagiarism detection. This then unlocked an online test using adaptive release. The online test required students to answer questions using the simulation that had just been completed. For example, to report key data from the simulation or to make an alteration to the simulation, reset and re-run the simulation and then to report the updated value of an output parameter. This approach draws on Ericsson’s ‘deliberate practice’ model, in that the tasks are well-defined, the work is difficult but doable, informative resources are provided to support learning and there is opportunity for feedback and repetition (Ericsson, 2008).

The first task was designed to introduce students to the basics of using PRO/II. Instructions were relayed via five short videos that outline seven basic steps of creating and running a simulation, illustrated with the very basic example of a mixer feeding into a pump. The seven steps outlined were based on those outlined in the PRO/II training manual and consisted of the following: 1. building the process flow diagram; 2. checking the units of measure; 3. defining components; 4. selecting the thermodynamic method; 5. supplying stream data; 6. specifying the process operating conditions; and 7. running the simulation and reviewing the results.

The second task was designed to introduce students to several unit operations in PRO/II, including the flash drum, shortcut distillation column, expander, Gibbs reactor and the simple heat exchanger (with hot-side utility, cold-side utility and two process streams). Instructions were again relayed via short instructional videos. These demonstrated specific requirements and settings for the basic use of each unit operation.

The third task required students to build a simple ammonia synthesis loop by following a set of outline instructions and guided by a figure showing what the completed PFD should resemble. The simulation included unit operations not previously introduced but no supporting videos were provided for these. Students again had to get the simulation working correctly before uploading it and taking a test based on interrogating and making changes to the simulation.

The forth task was broken into two parts. Firstly, students had to work in teams of four to reverse engineer an existing simulation by exploring how it worked. They then had to create a set of instructions containing enough information for someone with no prior knowledge of the simulation to build it from scratch. Nine teams each worked on a different simulation, including an Excel linked

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Fisher-Tropsh reactor, a pressure-swing distillation system, a crude distillation unit with side strippers, a more sophisticated ammonia synthesis plant, a plant for producing methyl tert-butyl ether (MTBE), a plant for separating isomers of xylene by crystallisation, a plant for the liquefaction of natural gas, a bioethanol plant and a chiller plant. Each team was directed to test out and make iterative improvements to the instructions before submission. During this process students received tutor support, including the creation of additional instructional videos to explain particularly difficult aspects of setting up a simulation. Once submitted, the instructions were made available to entire class and students were required to work on an individual basis to create five of the simulations. As with all previous tasks, students had to get the simulations working correctly before uploading the files. Again, this unlocked a test with questions based on modifying the simulation and reporting updated output data.

The fifth and final task asked students to do something “impressive” with PRO/II. It was suggested that they could create a process simulation of a complicated chemical plant, develop an educational wiki about process simulation, make a screencast or devise a tutorial about using an advanced feature in PRO/II. It was suggested that this task could be done in pairs, small groups or as an individual, with tutor support also being made available.

The coursework grade was based on marks from the tests associated with Tasks 1, 2, 3 and 4, with weightings of 5%, 10%, 10% and 75% respectively. Task 5 was presented as an opportunity for continuing professional development (CPD) and did not count towards the coursework grade.

2.2 Evaluation methodology

The teaching approach was evaluated by conducting an online survey, face-to-face interviews and by recording the tutor’s observations. Tutor observations were recorded by the author after teaching had finished but prior to conducting the interviews in order to prevent these observations being influenced by the interview responses.

The online survey was conducted in order to evaluate the student’s perceptions of using the instructional videos and structured coursework tasks to learn PRO/II. The survey consisted of six Likert scale questions, two multiple choice questions and one free text response question. It was hosted by the Bristol Online Survey tool and all responses were anonymous. A total of fifteen students (42%) participated in the online survey.

A series of interviews were subsequently conducted in order to gain greater insight into the student learning experience and with the aim of exploring the following research questions:

1. How did students perceive their experience of learning PRO/II?2. What factors in the learning process were important for skill acquisition?3. How did student understanding of process simulation develop as a result of undergoing the PRO/II

training?4. How do students perceive their own development in relation to the Dreyfus and Dreyfus five stage

model for skill acquisition?

All students on the module were invited to participate in the one-to-one interviews and all respondents to this invitation were interviewed. A total of six interviews were conducted by the author. Of the participants, two were female and four were male. Participants were provided with an outline of the questions in advance of the interviews in the form of a pre-interview questionnaire.

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Participants were also provided with lists of characteristics for someone at each stage of development in the Dreyfus and Dreyfus model (see the online supplementary material for details), to enable them to answer specific questions in relation their own development with reference to the model. These questions and supporting information were provided in order to allow participants more time to reflect on their experiences and to think about their responses before the interviews, with the aim of eliciting more fully developed answers. The questionnaire responses were collected for analysis along with the interview responses, but were not examined by the interviewer before or during the interview. A semi-structured interview format was adopted so as to ensure key areas were covered, whilst also allowing points raised during the interviews to be explored further. Whilst the student-teacher power dynamic could have influenced the participant responses, it was necessary for the author to conduct the interviews to facilitate the semi-structured format of the interview by allowing insightful follow-up questions to be asked. However, it should be noted that this potential for bias cannot be measured or corrected. All questionnaires and interview transcripts are available in the online supplementary material. The names of participants have been changed to protect their anonymity, as per the ethical approval for the project. The interview and pre-interview questionnaire responses were analysed using a thematic analysis, based on a template (King, 2004). The template was devised by identifying priority themes from the research questions posed. The list of themes was then developed, augmented and refined whilst the data was collected and analysed. All responses were stored, coded and analysed using NVivo 10 (QSR International); a software tool for the management of qualitative research data. Ethical approval for this project was granted by the School of Applied Sciences Chemistry & Forensic Science committee under reference SASEC-C-15-01.

3. Results and Discussion

3.1 Tutor observations

The students were observed to complete Task 1 reasonably quickly. It was a source of great satisfaction for the tutor to see students pick up the basic aspects of using PRO/II within such a short period of time, typically less than one hour. However, the possible downside to this is that students may have developed a sense of false confidence from their apparent “mastery” of the software in a short period of time. The problem is that Task 1 only scratches the surface of the software and, to their surprise, students did run into difficulties in later tasks.

When working on task 2, many students seemed astonished to find that there simulations did not run straight away. When they asked for help it turned out that they had only built the flowsheet and specified the streams and process conditions, whilst neglecting to follow the other essential steps outlined in the first task. For example, this included forgetting to specify the thermodynamic system. As soon as this was pointed out to them, many had an important moment of realisation – they had to follow all seven steps outlined in Task 1 and not just complete the basic setup for the unit operation demonstrated in the videos provided for Task 2.

Task 3 presented very little difficulty to students, since it successfully built on and incorporated competencies developed in Tasks 1 and 2. It was also excellent to observe that students felt confident enough to incorporate and specify unit operations that they had not previously encountered. This is a clear indicator that students were starting to break away from the need for context free rules (as required by novices) towards trying new things on their own (which is characteristic of advanced beginners) (Dreyfus et al., 1986; Hunt, 2008). The only error that became problematic and recurring within this task was when the splitter unit operation was incorrectly

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specified for the overall operability of the simulation. Here, the flowrate of the recycle stream was specified at a given value instead of the purge stream. This observation is again seemingly consistent with someone at the advanced beginner stage of skill development - they can start to break away from rigid rules but still have difficulty troubleshooting (Dreyfus et al., 1986; Hunt, 2008).

It also became apparent that some students initially struggled to troubleshoot problems that arose in Task 4. For example, if a simulation they had created failed to run, they were generally unable to trace the cause of the problem and had to start again by building the simulation from scratch. This could have been due to the cryptic nature of error messages presented by PRO/II and/or their lack of experience in dealing with such complex simulations. For the students that were able to troubleshoot errors in their simulations, this indicates that these students were moving towards operating at the competent level of skill acquisition (Dreyfus et al., 1986; Hunt, 2008).

Overall, the approach appeared to work well. Students picked up PRO/II very quickly and developed a good level of competence and understanding by the end of their first year. In fact, the breadth and depth of development appeared to go beyond that which the tutor experienced in his own undergraduate degree.

3.2 Online survey and interview feedback

In the first half of the year students had been taught the use of word processing and spreadsheet software packages via explanation and demonstrations from the front of the class, accompanied by the opportunity for them to try out what was shown afterwards. This experience was used as a comparator for the use of videos for learning new software. The questions in the online survey asked about the student perception of the teaching approaches used. The responses, shown in Fig 1, have been grouped in order facilitate comparison of similar questions.

The results in Fig 1a. indicate a preference towards the use of videos for learning how to use software, although the difference is only marginal. This preference for videos was also evident when students were asked if they had a preference for being taught by video tutorials or by demonstration/explanation from the front of the class - 40% had no preference, whilst 33% preferred video tutorials compared with 27% of those that preferred demonstration/explanation from the front (data not shown in Fig 1). This observation was further supported by the fact that when asked if they would recommend video tutorials for learning new software to others, 73% of participants said definitely and 27% indicated they possibly would. None of the participants opted for the ‘no’ option in response to this question (data not shown in Fig 1).

Respondents to the online survey were also asked if they felt the videos and coursework associated with Tasks 1 to 3 had helped them learn about and gain confidence with the basics of using PRO/II, see Fig 1b. All of those that participated in the survey agreed or strongly agreed that the videos and these coursework tasks had helped them learn the basics and to feel confident about the using the software at a basic level.

Once again, all respondents also agreed or strongly agreed that Task 4 had helped them feel confident about using some advanced features in PRO/II (see Fig 1c.), although the split was a less favourable than that observed for a similar question pertaining to confidence with the basics of using the software (see Fig 1b.), with more responses favouring the ‘agree’ option over ‘strongly agree’. Respondents were less sure that the videos had helped them learn about the advanced features of PRO/II, with only 87% agreeing or strongly agreeing and with 13% disagreeing (see Fig

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1c.). The results suggest that while videos are important for the development of basic skills, the development of more advanced skills relies less on videos and more on completing the task set.

When participants in the survey were asked “Do you have any further thoughts about the use of video tutorials for learning PRO/II?” only one person commented, saying that:

“The video tutorials are useful because you can look back at them and see what was done. However, they don't give you a clear explanation of the processes and you are unable to ask questions.”

In response to this comment, it is acknowledged that the videos were designed to be succinct and to the point so as to engage and keep students focused on the task at hand. However, this comment does suggest that there is a need for further contextual information within the teaching material to explain the functionality and scope of different features of the process simulation software. This could be particularly pertinent to moving students towards the competent and proficient levels of skill development.

Overall, the online survey gives an overwhelmingly positive impression of the impact that the course tasks and videos had on student learning. This view is also supported by a range of positive comments from the interviews, which included:

“I feel that PRO/II has been a valuable experience, which is relatable to real life scenarios and gives me insight into what my potential career could lead to.” – Katie

“I think it’s just been a good experience, I think the majority of people on my course who I’ve spoken to about PRO/II, I think they enjoyed using it.” – Sadiq

“The overall experience was very positive and I did enjoy working on it and I even value the skills now that I’ve used them properly. Yeah, I think it’s been very useful.” – Sophie

Although, the students also found the work challenging and difficult at times, for example, students also said:

“There were some parts where it was quite complicated.” – Jack

“The part that can be most frustrating with the software is fault finding. If running a simulation does not go as expected it can take hours to find the reason behind it.” – Simon

“I felt that I was thrown into the deep end because I’d never come across it before and I didn’t know what to expect and looking back really trivial things were looking like major things.” – Sadiq

3.3 Analysis of interview responses and proposed learning model

Detailed analysis of the interview responses and pre-interview questionnaires revealed a number of emergent themes for process simulation teaching and learning, these are: progression through stages of skill acquisition; phases of learning; meta-cognition and motivation; barriers to learning and developing knowledge. The various contributions of these themes have been brought together in order to form an overall picture of how student learning is happening, forming a proposed model for process simulation pedagogy. This model is shown graphically in Fig 2 and described in detailed in the following sections.

As would be expected, the video-enhanced and discovery-based aspects of the learning process also came through as distinct themes during the template analysis of the data. As such, key features of these have been captured and connected with the main themes in the detailed analysis below.

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3.3.1 Phases of learning and stages of skill acquisition

Students could identify with several stages of skill acquisition from the Dreyfus and Dreyfus model and they could link their development to specific tasks within the coursework. The analysis also revealed distinct phases in the learning process.

All students identified that they were at the ‘novice’ level of development when they started the coursework but that working on Tasks 1 and 2 helped them to develop towards the ‘advanced beginner’ stage of development. These tasks were clearly linked with an ‘early phase’ of learning, in which students learn by copying instructions in order to familiarise themselves with the software layout and tools. Within this phase the learning is self-paced and supported by videos, which allows students to consolidate their understanding before moving on. A distinct benefit of using videos is that information being transmitted can be controlled by the learner. Supporting responses for these observations include:

“During task 1 I was in the novice stage as I was starting to familiarise myself with the layout of the program and how to use the basic tools to place different pieces of equipment. This then enabled me

to progress further as I soon wanted to progress onwards on my own.” – Jack

“During task 2 a much larger perspective for the uses of the software became clear in the design of the unit processes and their functionality. I believe this was an essential step in moving towards

advanced beginner understanding of the software.” – Barney

“The ability to take it at your own pace rather than feeling rushed with someone saying do this, do that. You could pause, rewind, check back if you’d made a mistake, you know, which with software is

quite possible. ” – Simon

This ‘early phase’ relies on an expository-based approach to learning (following instructions to reach a predetermined/known outcome), which has been linked with the development of the foundational thinking skills within Bloom’s taxonomy (cognitive domain) of remembering, understanding and applying (Anderson et al., 2001; Domin, 1999). The cognitive domain of Bloom’s taxonomy is shown in Fig 3 along with explanations and links to the relevant learning phases and modalities.

Working on Task 3 was a bridge between the ‘early phase’ of learning and the next phase of learning. Students could identify themselves as consolidating their ‘advanced beginner’ level of skill and maybe even moving towards the ‘competent’ level of skill acquisition. Supporting responses for these observations about learning during Task 3 include:

“Task 3, I’d say I was, I would say advanced beginner, because stage 3 was a bit more challenging and it asked us to create a full process plant and it only gave us a few specifications. It wasn’t a step

by step how to do it, it was more here’s the information and get on with it.” – Sadiq

“Probably, still, advanced beginner. Maybe beginning to get some skills of the competent stage, but you’re still following instructions that were set for you, you weren’t trying to do anything yourself. It

was a more complicated kind of set up, than the other ones. It was getting increasingly more difficult.” – Sophie

“During task 3 I think you are still advanced beginner, still asking a lot of questions about how it worked, you needed someone there to keep an eye on you and respond to your trouble shooting, but

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I think task 3 was starting to push you towards the competent level but you weren’t there yet.” – Simon

When reflecting on Task 4, students felt they were either starting to develop traits of the ‘competent’ level of skill acquisition or that they were at ‘competent’ level of skill acquisition and some even felt they were starting to develop traits of the ‘proficient’ level of skill acquisition. The student experience of Task 4 was clearly linked with a ‘late phase’ of learning, in which students learn through discovery, supported by peer-assisted learning and problem solving. Students felt that having to reverse engineer an existing simulation to create a set of instructions they gained more detailed and explicit knowledge of the software and its uses. Supporting responses for these observations include:

“To be able to have free reign of the software and have to dig and find all the different specifications ourselves and recreate, that meant you had to look into the software. You couldn’t just, sort of, be

instructed. It was about your own discovery of how to do things.” – Simon

“…when I got to task 4 I kind of branched out to the proficient, because I was learning from the experience of others, I was understanding the context of the whole program and previous problems. I could build all the plants much more quickly and without really any issue, I wasn’t searching around

for ages trying to find things.” – Jack

“I would say I was definitely competent by task 4. I was able to kind of help people who maybe weren’t as far ahead as I was or when things went wrong, kind of trying to trouble shoot instead of asking you or someone else. Taking it apart and kind of splitting it into bits and trying to build them

again really helped I think because you got to know every little bit of the system.” – Sophie

This ‘late phase’ can be seen as relying on combination of discovery- and inquiry-based learning modalities, since the students were required to investigate and explore problems rather than simply being given the information to remember and understand or simply being provided with procedures to follow and apply (Alfieri et al., 2011; Justice et al., 2009). Furthermore, inquiry-based learning has been linked with the development of higher-order thinking skills from Bloom’s taxonomy (cognitive domain), including analysing, evaluating and creating (Anderson et al., 2001; Domin, 1999; Justice et al., 2009). The links between these learning modalities and thinking skills are shown in Fig 3 within the context of the cognitive domain of Bloom’s taxonomy.

A final ‘future phase’ of learning was also identified in the analysis of the interview responses. Within this phase it is envisaged that students will extend their experience of using the software with more complicated simulations; enhance their skills, particularly in troubleshooting; and potentially extend their knowledge of process simulation, for example, in how to select appropriate thermodynamic calculation methods for a given simulation. This ‘future phase’ is aligned with the notion that further experience and development would potentially lead to achievement of an ‘expert’ level of skill acquisition. The concepts for this phase of learning emerged from the analysis of a range of ideas and themes within the interview and questionnaire responses, the following quotes are typical of those drawn upon and exemplify some of the features of this ‘future phase’ of learning:

“I wouldn’t say I got to the expert stage, definitely not. I think I’d need to do a lot more, do some, maybe much bigger, larger scale projects that were a lot more complicated, made with lots more

components.” – Jack

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“I feel like maybe the same kind of set up, but kind of just the next step, maybe more complicated systems, or maybe something that had gone wrong and you had to troubleshoot it to find out

yourself why it had gone wrong, that might be quite helpful too.” – Sophie

“The main thing that I would have like to know more about was the thermodynamic properties aspect of the software. This possibly could have been improved if we had a lecture looking at the thermodynamic properties and how they effected the process when changed. This could possibly have been done by teaming up with physical chemistry to teach some of the principles associated

with the property options.” – Simon

Whilst most students conceded that had not undertaken anything significant for Task 5, they could see that it would have been a useful bridge towards further development of their skills and understanding. As such, this ‘future phase’ of learning is also strongly connected with the concepts of lifelong learning, self-regulated learning, self-motivation and self-efficacy (Zimmerman, 2002); attributes that align well with becoming a professional and the continual self-development that this requires.

3.3.2 Meta-cognition and motivation

Throughout the interview and questionnaire responses, students exhibited a good degree of meta-cognition and also revealed aspects of why they were motivated to learn process simulation. The students realised that they needed to pay careful attention to all of the details required in the simulation and demonstrated an awareness of their own limitations. The students also demonstrated the desire to learn more about the software and could see the importance of the skills they were gaining in terms of employability. Furthermore, the students were able to reflect on and recognise their development and achievements; this included identifying the progression of their learning through the structured and linked nature of the coursework tasks. All of this was also supported by the personal interest and enjoyment that students derived from participating in the learning process. Typical comments supporting these themes include:

“Yeah it’s made me realise how far I’ve got with the software. And now I think back to it from where I began its quite, quite impressive how far I’ve come and quite a nice progression.” – Simon

“The workshop itself was an excellent way to push me into becoming a competent user, like, within, I don’t know if we do much PRO/II in the second year but if we do I feel like I’ve definitely got the

basics down ready to push onto harder development and to become a more competent user and to fully step out of the advanced beginner stage, I think it pushes you to the best of your ability.” –

Barney

“To be honest with you I didn’t think that task 1 would affect what I’d be doing in task 2 or 3 but having completed task 1 and then 2, I could see why we did what we did in task 1 before task 2, and then task 3 so it all built up as we went along, built up the knowledge and yeah I can see that I was

dealing with something that I hadn’t dealt with before so I was a bit worried but the instructions and the help given made it easier.” – Sadiq

“I also began to see how important and relevant it was, for me as a potential future process engineer, to have the understanding and skillset to use programs of this type.” – Sophie

The importance of motivation and meta-cognition for effective learning have been documented previously (Bates, 2016; Case and Gunstone, 2002; Cross, 1981; Krathwohl et al., 1964; Liu et al.,

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2012; Zimmerman, 2002). For example, Bates (2016) summarises a number of motivational theories and draws out several important factors to aid motivation for learning, including access to resources, student input to the learning process, setting of learning objectives and provision of helpful feedback. Furthermore, the affective domain of Bloom’s taxonomy (see Fig 4) sets out how learners can move from passively receiving information to actively valuing and internalising the knowledge and skills they are developing, until they reach the point where they are characterised by the subject (Krathwohl et al., 1964). The aim is for learners to move from simply being consumers to becoming the embodiment of their subject i.e. they go from studying their subject to being identified as professional within the discipline. In the broader context of this work, the aim is for students to go from just studying chemical engineering as a degree subject to becoming a professional chemical engineer.

3.3.3 Barriers to learning

For the ‘barriers to learning’ theme, several potential obstacles to learning emerged, these included software availability, group work, troubleshooting and session spacing. Students suggested that learning how to use the software would have been easier if it had been easier to access outside of class time and if the initial teaching sessions had been closer together. There was also a perception that work and contributions were not always divided equitably among group members for Task 4. In terms of skill acquisition, students felt that the whole area of troubleshooting was something they struggled with and needed further support with. Typical comments supporting these themes include:

“The only downside was maybe the fault finding side of things. If you did something wrong finding that could take you ten times as long as it was to make it. The software is that advanced that you have to look back through every single detail and maybe the approach, how to do that could have

been better, sort of, shown to us before we started.” – Simon

“I know that if it was more accessible and on more of the university’s computers it would be much easier to practice and get a full understanding of the overall program and how it works.” – Sophie

“I encountered more problems in Task 4 than any other tasks as it was group work and not everyone in the group decided to contribute as much as they could have done.” – Sadiq

“There were some parts where it was quite complicated where… I think for me the most complicated part was the distillation column in one of the later tasks, task 4. Setting up the different trays, each of

their settings I found – if you made an error, it was hard to find that error…” – Jack

Many of these barriers to learning have been previously documented and are recognised as potentials issues in the learning process. As outlined in section 3.3.2, access to resources is an important factor to support motivation for learning (Bates, 2016). Concerns around group work in educational settings are perennial and multifaceted, and there exists a range of perspectives and analysis on routes to supporting successful learning through group work (Burdett, 2003; Lizzio and Wilson, 2006; Myers, 2012), however, a full analysis is beyond the scope of this article. In contrast, troubleshooting in the context of teaching process simulation has received little attention and, as such, it is an area with great scope for further exploration.

3.3.4 Developing knowledge

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Through the course, students developed knowledge pertinent to process simulation that was not necessarily explicit within the coursework tasks. Through the interview and questionnaire responses it became apparent that students had started to appreciate the importance of process simulation for industry, this was particularly highlighted by an awareness that simulations could contribute to economic and safety assessments of plants before they were built. Whilst students recognised they had limited knowledge in selecting the thermodynamic method for a simulation (see section 3.3.1), they did appear to develop an understanding of the importance of selecting an appropriate method in order to obtain meaningful results. The students also began to recognise that process simulation is a powerful tool for chemical engineers to use. Typical comments supporting these themes include:

“From using the software I have realised how time consuming it can be to construct a system, however I have realised that with a simulation, factors can be changed much more quickly than in a

real system, which is not only safer, but less cost in terms of time and money.” – Katie

“It shortly became clear of the magnitude of the PRO/II simulator applications in chem eng down to the alterations of different thermodynamic models and their effects on modelling anything from a

unit process up to designing of entire chemical plant.” – Barney

“I understand it’s a very powerful tool. I think to use it you’d have to spend a lot of time with it. What we know is sort of a foundation but the implications of understanding the software are massive. To go into a company and be able to say if we did this to the plant it would do this and show them on a

digital model like PRO/II would be invaluable. It would save on obviously costs and time and everything for that company.” – Simon

The concept of developing knowledge is synonymous with the foundational levels in Bloom’s taxonomy for the cognitive domain. Such foundations are important, because they provide the basis upon which higher order thinking skills can be built upon, see Fig 3. This theme also highlights that the students developed an appreciation for the value and utility of process simulation, which links with the motivational aspects of learning discussed in section 3.3.2.

3.3.5 Learning model summary

From the above in-depth analysis (in sections 3.3.1 to 3.3.4) it can be seen that learning occurs in several distinct phases, is supported by student motivation and meta-cognition, can be hindered by several possible barriers to learning and is underpinned by the development of knowledge. The phases of learning are linked with varying levels of cognition and skill acquisition. The detail of these observations and findings provide a rich picture of how students can develop a good level of skill in process simulation, forming an overall learning model for process simulation pedagogy.

3.4 Zone of proximal development

The overall methodology described here can be viewed as building on Vygotsky’s ‘zone of proximal development’ model (Chaiklin, 2003), in that it encourages and supports students to move from things that they can do (i.e. using a PC) to things that they could not do before (i.e. using software that was previously completely unknown to them) via guided instruction. The wider context in which the methodology was used also encourages and enables students to progress onto completing harder and more challenging tasks. These tasks are harder and more challenging in as far as they would have been incomprehensible or impossibly difficult without first receiving the guided instruction.

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3.5 Results overview

There appears to be good agreement between the different data sources regarding a number of facets of the learning process. It is clear that the use of videos for learning process simulation is well suited to supporting the development of basic skills. It is also apparent that the development of higher order skills is very effectively supported by discover/inquiry-learning approaches. The use of well-structured programmes of learning activities allows students to progress through several stages of process simulation skill acquisition. The ability of students to troubleshoot problems in a simulation is a potential area of weakness that needs to be examined further.

It is also pertinent to note that the materials presented here have started to be used by members of chemical engineering teaching staff to learn how to use PRO/II, with positive feedback. Whilst this has not been examined in any detail in the present work, it does indicate that packaging training for new software in this manner might be one way to overcome the reluctance of faculty members to learn how to use new and complicated pieces of software; an issue identified by Dahm et al. (2002) as a potential barrier to the effective teaching of process simulation.

One potential criticism of the approach adopted here is that students are able to solve a problem using the simulator without really understanding what is going on. However, as the students developed and became more advanced users of process simulation they began to demonstrate a clear desire to understand more about underlying principles. Furthermore, process simulation is only a tool within chemical engineering; to fully understand and appreciate what is going on requires the depth and breadth of knowledge that can only be developed from studying the subject at degree level and beyond. Therefore, whilst the suggestion that students are only using simulation to solve problems without fully understanding them may hold some credence, it should not become an excuse for abandoning the training students to become advanced users of simulation software. On the contrary, as can be seen from this study and the Dreyfus and Dreyfus five stage model of skill acquisition, it is only when someone moves towards higher levels of skill acquisition that they are ready for the bigger picture and develop a desire for greater understanding of the context within which they are operating (Dreyfus et al., 1986; Hunt, 2008). However, students should be made aware of their limitations and the software’s limitations i.e. the results will only be as good as the information supplied – “rubbish in equals rubbish out”. This issue adds weight to the arguments in the literature that process simulation training should be closely integrated with the wider chemical engineering curriculum. It is envisaged that such an approach would work particularly well in later years of a degree course.

4. Conclusions

The work presented in this study demonstrates that students can develop a good level of skill and understanding in process simulation during their first year in a relatively short period of time. Carefully structured coursework tasks, supported with videos, allow students to acquire process simulation skills at the advanced beginner level. Progression onto more challenging, discovery/inquiry-based tasks allows students to consolidate their skills as advanced beginners and to progress towards developing characteristics consistent with features of the competent and potentially even proficient levels of skill acquisition. Furthermore, the thematic analysis of detailed qualitative research data gathered for this study has allowed a new learning model to be proposed for process simulation pedagogy. However, there is a need for further work on process simulation

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teaching and learning in order to address a number of issues raised by this study. Further learning activities need to be identified to allow students to consolidate and progress their level of skill and understanding in process simulation. These might include similar but more challenging activities to those outlined here. To develop a full appreciation for how process simulation fits within the broader chemical engineering context, the activities should be integrated and embedded within the wider curriculum of a degree programme. These activities should also be designed to expound the capability and utility of the various unit operations available in PRO/II, whilst at the same time, challenging students to think about why certain options and decisions are being selected and made during the creation of a simulation. For example, students should be challenged to think about why a particular thermodynamic system is being used for a given process. Activities around developing troubleshooting skills also need to be considered. The conceptualisation of these further developments will benefit from the foundations laid by the approaches to process simulation pedagogy developed and described in the present study. Furthermore, the work presented here offers students and teachers alike the opportunity to reflect on their own practices and approaches to teaching and learning, with the potential to aid meta-cognitive skill development and to potentially enhance their view of the need for life-long learning.

Supporting Information

Electronic supplementary material can be found online includes all of the interview transcripts and questionnaire responses and a document summarising the characteristics and features for different stages of skill acquisition.

Acknowledgements

The author is grateful to the students that participated in the online survey and interviews; to University of Huddersfield for supporting this work; to Grant Campbell (University of Huddersfield) for useful discussions about chemical engineering education; Schneider Electric for providing an academic licence for PRO/II; Yasser Ahmed (Schneider Electric) for advice on using PRO/II; Jane Bradbury (University of Huddersfield) for advice about qualitative research design, ethical approval and data analysis; John Fletcher (Aston University) for useful discussions about models of skill acquisition; Abigail Shuttleworth (University of Birmingham) for assistance with using NVivo and transcription of interviews, and James McDowell (University of Huddersfield) for useful discussions about video enhanced learning.

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Fig 1. - Responses to the online survey Likert scale questions: a. responses to questions about learning approaches, b. responses to questions about learning basic features of PRO/II, and c.

responses to questions about learning advanced features of PRO/II.

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Fig 2. - Proposed learning model for process simulation pedagogy and how it relates to different levels of cognition, skill acquisition and learning modalities.

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Fig 3. - Bloom’s taxonomy for the cognitive domain of learning, with linkages to learning phases/modalities (Anderson et al., 2001)

Fig 4. - Bloom’s Taxonomy for the affective domain of learning, with summary explanations for each level (Krathwohl et al., 1964)

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