Hulstijn (2003) Incidental and Intentional Learning

So this is one of the papers I thought I needed to read as follow up to Laufer & Hulstijn (2001) as  described in an earlier blog post.   My colleague had been recommending that I read a paper by Hulstijn that apparently addressed a construct of 'rehearsal' that could be applied to intentional learning.  Unfortunately I grabbed the wrong book chapter.  I should have been reading Hulstijn's (2001) paper entitled: "Intentional and Incidental Second Language Vocabulary Learning: A Reappraisal of Elaboration, Rehearsal and Automaticity", a book chapter in Cognition and Second Language Instruction; but I accidentally grabbed Hulstijn's (2003) "Incidental and Intentional Learning", a book chapter in the Handbook of Second Language Acquisition.  Anyway, I'll read the other paper next week, but since I read all of Hulstijn's 2003 paper before realizing my mistake, here are my notes on that. Actually I did notice my mistake earlier, but I was in a park with the paper printed out, and I couldn't easily grab all of the 2001 paper on my iPhone, so I finished reading the paper copy I had printed out.

In this paper Hulstijn undertakes a detailed examination of the differences between incidental and intentional learning from popular, psychological and second language acquisition perspectives.  The popular perspective is that of study versus immersion, where incidental learning corresponds to the idea of immersing oneself in a language by extensive communication with native speakers and reading/writing of native texts; and intentional learning corresponds to attending language classes.  These popular perspectives only partially reflect the way in which the terms are used academically.  After reviewing the development of 20th century psychology, Hulstijn says that in terms of cognitive psychology that:

intentional and incidental learning refer, strictly speaking, only to the presence or absence of an announcement to participants in an experiment as to whether they will be tested afterwards

I had been surprised when Folse (2006) had classified his study as incidental, when it had seemed to me that asking subjects to perform cloze tests involving a limited set of words, or construct sentences with those words, was tantamount to asking those subjects to learn those words.  To be fair to Folse, his study did involve a number of distractor tasks, but I was still surprised by the suggestion that simply not mentioning in advance that there would be a vocabulary test would make the study one of incidental learning.  I guess the common incidental learning study task is reading comprehension, where the task focus is much more clearly on something other than vocabulary acquisition.  I guess Folse's intention was to avoid having subjects focus on acquiring vocabulary so that they would not attempt to memorise the words through other means, such as writing down lists of words to look at independently, or repeatedly going over the words in their heads.  Of course Folse doesn't say this explicitly, but it makes me think of Eyssenck's (1982) assumption that active intention to learn does not tend to affect learning outcomes (backed up by experimental results of Hyde & Jenkins, 1973).  Although this leaves me confused as to the point of the incidental/intentional learning distinction.

Fortunately Hulstijn goes on to cite a number of more detailed definitions from Schmidt (1994a) as characterising the Second Language Acquisition perspective on incidental learning.  The three definitions in order of increasing specificity are:

1. learning without the intent to learn
2. learning of one thing while the learner's primary objective is to do something else 
3. learning of formal features while an individual is focused on semantic features
   

Going on to consider intentional learning from an SLA perspective Hulstijn refers to the cognitive interpretation of the use of rehearsal and memorizing techniques employed by learners when they have explicit intention of learning and retaining lexical information (Schmitt, 1997).  He does not re-iterate the suggestion from Laufer and Hulstijn (2001) that the benefit of incidental over intentional learning experiments is that the experimenter can be more certain of the strategies being employed by the learner.  The distinctions between the incidental/intentional and explicit/implicit dichotomies are re-iterated and Hulstijn summarizes the difference between explicit and intentional learning as follows:

Whereas explicit learning involves awareness at the point of learning (e.g., by trying to understand what the function of a certain language form is), intentional learning involves a deliberate attempt to commit new information to memory (e.g., by applying rehearsal and/or mnemonic techniques)

Hulstijn suggests that there is a confusion over the what and how of incidental and intentional learning; that it is almost impossible to conceive of the acquisition of grammatical features through intentional learning.  This seems a little odd for me, but then grammar is not my specialty.  Still I can remember being explicitly instructed on points of Japanese grammar and then trying to employ them.  In some ways it seems to me that one can intentionally focus on learning grammar and lexis, but that employing both effectively requires achieving a level of automaticity where the process through which they are employed is not subject to introspection.

The involvement load constuct of Laufer & Hulstijn (2001) is only mentioned briefly in the context of incidental learning of novel words, and there is no mention of its possible relation to intentional learning.  My feeling is that my colleague Yi-Jiun Shiung's intuition that the constructs of involvement load can be applied to intentional learning are correct; although I guess I start to see that the incidental/intentional dichotomy is being used to distinguish task type.  If you are constructing a sentence or solving a cloze test then you are, a priori, not repeating a particular word over and over in your head, or constructing a mnemonic to help you remember an L1-L2 mapping.  Thus for the purposes of our meta-analysis it seems that classification in terms of task type is perhaps more important than the incidental/intentional dimension.

The constructs that Hulstijn refers to in his section on intentional learning studes are monolingual vs bilingual, context vs. no context, rehearsal, feedback, and mnemonics.  Interestingly he also says that:

It appears that a number of researchers have investigated various presentation and rehearsal regimes (with and without feedback) in computer-aided instruction, but such studies are almost never published in international journals.

I think we have managed to find a few, although many number of them are post-2003.  Of greater significance are Hulstijn's summaries of the findings of various studies including the indication that learning rates under genuine incidental learning conditions are low compared to intentional learning conditions.  Although of course it is almost impossible to control for 'time on task'. Other studies suggest bilingual presentation outperforms monolingual presentation (although I assume this depends on learner level and the test type), that context is a difficult construct to pin down, and mnemonics should only be resorted to when other approaches have failed.

There's a study by Griffin & Harley (1996) that arguably should be in our meta-analysis, as well as a few others we should check on, particularly in the latter section on intentional learning.  Of course this raises the question about whether we try to broaden the scope of our meta-analysis, or whether we compromise it by including other studies that we discover as a result of reading the core set of initial papers that we found.

My References

Folse, K. S. (2006). The Effect of Type of Written Exercise on L2 Vocabulary Retention. TESOL Quarterly, 40(2), 273-293.

Hulstijn's References

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Sycara (1991) Problem Restructuring in Negotiation

I read about this paper in Sycara's (1998) Multiagent systems paper, which I blogged about recently. In that blog post I was saying that I found it really difficult to imagine artificial agents handling negotiation in anything other than an inflexible and trivial fashion. Now I have read Sycara's (1991) paper "Problem Restructuring in Negotiation" which describes her PERSUADER system, I have a much better idea of how an expert system can be structured to handle negotiation. The PERSUADER system is a very thorough expert system that encapsulates knowledge from real world experts, and uses Case Based Reasoning, Goal Graph Search, Situations Assessment and Persuasive Argumentation to mediate labour disputes. The mediator agent maintains dynamic belief and goal networks for the negotiating parties and performs search over these networks in order to determine likely settlements.

The PERSUADER system is complex, and I won't attempt a detailed description of it here, but suffice to say that it feels like the process of negotiation performed by humans has been dissected and analyzed and broken into its component parts. As soon as I had caught the gist of one component, e.g. matching case histories of settlements with the current negotiation, when we were on to other approaches that involved generating settlements from scratch by reasoning about the goals and beliefs of the negotiating parties. I would have thought that even small parts of the system would be very complex, but the paper goes on and on to describe more and more involved meta-strategies that are designed to resolve different types of conflicts. It also seems that the system can learn based on the failure of proposed settlements as they are added to it's memory of cases, and the goal and belief networks of the negotiating parties are updated, although I didn't read specific details about this latter aspect.

I guess my main questions were has this system ever been evaluated in a real world context, and did anyone ever use it? It seems that the PERSUADER was developed as part of Sycara's PhD thesis, so I may need to read that and other subsequent publications to get to answer my questions here. I was able to grab a couple of other of Sycara's publications on PERSUADER from Google Scholar, but I couldn't find any mention of evaluation. In her classic paper on multiagent systems Sycara (1998) mentions that one of the key obstacles to the adoption of agent technology was individuals becoming comfortable with the idea of delegating a task to an agent. I find it difficult to imagine either party in a real labour dispute being willing to accept an AI expert system as a mediator; although not impossible - I would be very interested to know if such a thing had ever happened. There are hints in this paper that PERSUADER was applied to real world scenarios, but it is not clear if this was as part of a real negotiation, or after the fact.

Sycara mentions in passing knowledge-based work that provides support for human negotiators (Kersten et al., 1990; Jarke and Goeltner, 1987), and it seems that this is the kind of approach that is much more likely to see real usage. I sense a parallel with machine translation and machine support for human translators.

Even with the overall complexity of the PERSUADER system there are some issues that appear glossed over such as the functions that generate the bargaining power of a local union, which takes arguments of the economic context and the international union and returns numerical values. However I was impressed that nine months were spent filling the case base from labour relations literature, published arbitration awards, two human experts and the systems own learning mechanism. There is a saying that as soon as a computer does something we say that intelligence is not required for the task, thus making artificial intelligence an unreachable goal. Reading about the PERSUADER I felt that this was a carefully crafted expert system that encapsulated many of the techniques that would be employed by a human negotiator, but was also one that might miss many of the subtleties of human negotiation where agents are not always rational. It seems like the system would work well to the extent that it's model of the problem space was well designed, but in some ways that is the real trick isn't it. I get the feeling that really intelligent human negotiators are modifying the classification categories over time. Sort of what I am trying to do with our meta-analysis of second language vocabulary acquisition. There are existing categorizations implicit and explicit in the various papers, and they are not always perfectly aligned. We are trying to dig down to the information bearing categorizations through a process of adjusting the granularity of representation. Perhaps it's just my imagination, but it feels like that's the kind of thing that would be required for the system to impress me with its intelligence. Although at the end of the day any of these kinds of expert systems that are comprehensible to humans are going to feel a little like the emperor with no clothes; that there are sets of mindless computations going on and no real understanding, but that's a separate debate.

Bottom line here for my purposes is that I can certainly see what Sycara is talking about when referring to the literature on what Agents have to offer, and it certainly informs my "agents and p2p" invited paper, where I had been struggling to understand what agents had to offer. Sycara points out that the restructuring process and algorithms of PERSUADER are domain independent, and these are certainly a technique like a P2P distributed hash table, or search pruning that the field of agents has in its armory. On the flip side this seems like not such a good example because the question immediately arises how do we know the techniques employed by PERSUADER are any good? I guess that I should assume that different approaches to expert systems have been evaluated at some point in the literature and that the fields of AI and agent practitioners are aware of these results. However this kind of heavy reasoning just doesn't seem of much practical use in the P2P field; which now appears to me to have much more in common with reactive agents (as Sycara (1998) describes), which have the advantage of speed making them useful in rapidly changing environments. Interestingly Sycara (1998) says that:

In reactive systems the relationships between individual behavior, environment and overall behavior is not understandable, which necessarily makes if hard to engineer agents to fulfill specific tasks.

Although I feel that many P2P systems have done a pretty good job of engineering desirable system level behavior; and it is still not clear to me that reasoning agents with complex representations bring huge benefits. I guess they may in auction systems running trading software, but I am not aware of many plug and play expert systems that can be used for helping with general programming. At least PERSUADER feels like a giant heuristic - maybe it has some reusable parts, and if I read more recent papers on expert systems I would see more clearly the AI equivalents of distributed hashtables or swarmcasting.

It also occurs to me that one of the significant challenges is getting all the data into the expert system. It seems like a lot of effort it spent turning data from experts and historical data into a form that the machine can process. Being able to extract that info from conversations and reading documents (which are huge AI problems themselves) would have a big impact on the practicality of this kind of approach.

This paper inspired me to search for ruby code that supported AI techniques like Case Based Reasoning. I found this interesting blog post.

Cited by 92 [ATGSATOP]

Sycara (1998) Multiagent Systems

I am reading this paper as part of trying to put together a long overdue proceedings for the Agents and Peer to Peer Computing (AP2PC) workshop that was held in Hawaii in 2007. This is a classic paper presenting key ideas in Multi-agent systems (MAS) and research work that had addressed them. The paper is cited by 588 [ATGSATOP]. The first thing that strikes me in the abstract of the paper is that the author says:

the need for systems that consist of multiple agents that communicate in a peer-to-peer fashion is becoming apparent

What's striking is the use of the term peer-to-peer, before Napster, Gnutella and similar systems were released, spawning the field of P2P research. This paper reminds me that agent research has a strong AI theme and Sycara talks about how given particularly difficult problem domains, the only solution is:

to develop a number of functionally specific and (nearly) modular components (agents) that are specialized at solving a particular problem aspect. This decomposition allows each agent to use the most appropriate paradigm for solving its particular problem. When interdependent problems arise, the agents in the system must coordinate with one another to ensure that interdependencies are properly managed.

Which makes me think that the key point here is about systems having loosely linked independent modules. Sycara says that:

Currently, agents on the internet mostly perform information retrieval and filtering. The next generation of agent technology will perform information gathering in context and sophisticated reasoning in support of user problem-solving tasks.

And it seems to me that this next generation of agent technology has not come to be widely used by those outside academia. I think this is the key theme of Hendlers article ...

Sycara describes the characteristics of MASs as follows:

1. each agent has incomplete information of capabilities for solving a problem and thus a limited viewpoint
2. there is no system global control
3. data are decentralized
4. computation is asynchronous

Interest in MAS research is motivated by

1. Solving problems too large for a single agent (resource limitations, avoiding bottlenecks)
2. Supporting interconnection of legacy systems (agent wrappers)
3. Providing solutions to problems that naturally decompose into interacting agents, e.g. calendar scheduling
4. Providing solutions to the efficiently use spatially distributed information sources, e.g. in sensor networks
5. Providing solutions in situations where expertise is distributed, e.g. healthcare
6. Improving performance through concurrency, reliability, extensibility, maintainability, responsiveness, flexibility, reuse

This last point about improving maintainability and reuse makes me think about the field of software maintenance that Philip Johnson mentioned to me. I don't know what extent there has been a dialogue between the two fields, but it feels a little as if the promise of maintainability and reuse in multi-agent systems was made in something of a vacuum. It feels like there is an issue of maintainability and reuse in academic software, which is different from that of industrial software. Or maybe it's not so different, but maybe the problem of getting a reliable behaviour out of a system of agents was much harder than imagined. I mean it sounds great in principle, upgrading the system is just a question of inserting new agents, but knowing how an injection of new sorts of agents will effect the overall system dynamic seems extremely challenging. From my perspective it seems that developments in test driven development (TDD) and behaviour driven development (BDD) are simpler steps that have been more directly addressing the core problem of maintainability and reuse.

Sycara lists what she sees as the key challenges facing MAS in 1998 (would be very interesting to find out what subset she thinks remain 10 years later):

1. How to formulate, describe, decompose and allocate problems and synthesize results among a group of agents
2. How to enable communication and interaction between agents, and allow them to find one another in an open environment
3. How to ensure coherent actions and decision making, and avoid unstable behaviour
4. How should agents represent and reason about the states of other agents
5. How to recognize and resolve conflicts between agents
6. How to engineer and constrain practical distributed AI systems

Sycara then goes on to look at a number of topics in detail. Individual agent reasoning is seen as deliberative, reactive or hybrid. Agent organizations can be hierarchical, expert communities, market-based or scientific communities. The contract net protocol (CNP) is cited as an example of a solution to the task allocation problem; the challenge of dividing up a set of tasks between multiple agents so as to minimize conflicts and communication overhead.

Multiagent planning apparently often relies on a single sophisticated agent to do complex reasoning, although the functionally accurate model (F/AC) uses techniques such as partial global planning (PGP) to get away from this. Other approaches such as joint-intentions framework (all agents must agree shared beliefs and commitments) and SharedPlan (agents have intentions that prompt action or communication) have improved? on earlier work, while STEAM is a hybrid approach that combines joint intentions with SharedPlan.

Recognizing and resolving conflicts seems to be a core area for Sycara, main approach is negotiation but alternatives include backing up propositions to their assumptions, evidential reasoning and argumentation, constraint relaxation and social norms. Regarding negotiation Sycara refers to her PERSUADER system and the game theoretical work of Rosenschein, but I find it really difficult to imagine artificial agents handling negotiation in anything other than an inflexible and trivial fashion. However I have Sycara's PERSUADER paper and another by Rosenchein queued up and I should be reading those next.

Sycara considers other areas such as modelling other agents, managing communication and resources, and adaptation/learning. It always seems to me that modelling other agents would create some sort of complexity feedback loop. Managing communication and resources seems to be an area of huge overlap between peer to peer and agents. Sycara's distributed constraint heuristic search (Sycara et al., 1991) seems like an important forerunner of developments in the peer to peer field. In the section on adaptation and learning Sycara concedes that:

Enhancing the decision-making abilities of some of the individuals in the system can either improve or severely degrade overall system performance.

And this is apparently because complex behaviour can be exhibited by even simple computational eco-systems; thus the addition of learning/adaptivity can have a variable effect, and I would have thought the same applies to giving agents the ability to model each others beliefs.

In a section on applications Sycara lists multiple industrial applications such as multiple vehicle monitoring (Durfee, 1996), manufacturing systems (Parunak, 1987), monitoring and diagnosing faults in nuclear power plants (Wang & Wang, 1997), spacecraft control (Schwuttke & Quan, 1993), climate control (Huberman & Clearwater, 1995), air traffic control (Ljunberg & Lucas, 1992), management of financial portfolios (Sycara et al., 1996) and telecommunications (Weihmayer & Velthuisjen, 1994).

In conclusion Sycara says that:

Designing and building agent systems is difficult. They have all the problems associated with building traditional distributed, concurrent systems and have the additional difficulties that arise from having flexible and sophisticated interactions between autonomous problem-solving components. The big question then becomes one of how effective MASs can be designed and implemented.

She describes three key issues as impeding the widespread adoption of multiagent technology:

1. Lack of a systematic design methodology
2. Lack of widely available industrial strength MAS toolkits
3. Lack of comfort with the idea of delegating responsibility to autonomous agents

I would be very interested to see what Sycara's current perspective on the field is, although I think there is general agreement that multiagent systems have not so far achieved widespread adoption, and in fact how to address that problem appears to be the theme of a recent issue of the Journal of Agent Oriented Software Engineering and the introduction to that issue concludes that industrial adoption will be amplified if the following steps are taken:

1. Better communication with industry. Focusing on incremental introduction of agent concepts, and in particular, building on conventional methods and terminologies, and translating concepts and techniques from multi-agent systems into conventional software engineering practice.
2. Improve the connection with the conventional software engineering community. Agent researcher could present their work in conventional software engineering venues, and participate actively in related research initiatives.
3. Broaden the research agenda. In particular:

• Focus on goal-oriented design. Goals are what make agents adaptable, and adaptability is a major concern of today software systems.
• Enhance research on architectural patterns and styles. Patterns embody know-how in an established form and allow architects to adopt a particular multi-agent system approach.
• Extend research on validation and verification. Guarantees about the stakeholder requirements is a prerequisite for industrial adoption of multi-agent systems.

What I take away from all this is that while I find it difficult to imagine how agent programming or multi-agent systems could be of much help anywhere, I am often thinking of web application systems or other strongly user-oriented systems, when of course there are huge amounts of programming going on to create software to control chemical plants, supervise manufacturing and so on. It does seem plausible to me that an agent-based methodology can help in those environments, however when I reflect on it I realise that I am really not so interested in industrial software. I am really interested in the human computer interface, and it is there in general, and in web applications in specific, that multi-agent systems don't have much to offer. In some ways it seems like the agent hype getting all mixed up in the web hype was not such a good thing. Of course interface design is an area where the concept of agency and trust is very important, but theoretical results about the stability of populations of interacting agents don't shed much insight into human computer interface design, and I think all the years I was researching agents and peer to peer, what I was really interested in was interfaces. It's taken me a long time to work that out :-)

I guess the other thing I take away is how the huge swathes of DAI/Multiagent research had relatively little influence on the developers hacking up the first P2P systems and that was quite difficult for academic researchers to bear. At least that is my perception. I think this often happens - for academics who are aware of whole fields of research to see people implement things independently from that research, but the bottom line is that it takes a huge amount of effort to become aware of the available literature for a field and even more to infer design guidelines. I think there is an issue here relating to the accessibility of academic research, or perhaps it is just a more general one about older people wanting younger people to gain the benefit of their experience, when the younger people just want to try things out for themselves :-)

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Android Permissions and Audio

Found a list of all possible permissions:

http://developer.android.com/reference/android/Manifest.permission.html

would be nice if when you were trying to do something without permission it would tell you which one was missing.

Given that the example MediaRecorder code in the Android docs didn't work I was forced to search the Google Android Group for tips, and found this post with code that worked.

The challenge was then to see if the sound was being stored and I eventually found that it was when I pulled the sound file off Android with (of course I had to get the eclipse emulator to load the relevant sdcard by adjusting the launch configuration first):

./adb pull /sdcard/test.3gpp test.mp3

again the emulator seems to be available for attachment randomly ... usually the following returns nothing:

samuel-josephs-computer-2:tools samueljoseph$ ./adb devices
List of devices attached
emulator-5554    device

I did find a good summary of SD Card creation for Android.  The next challenge was to load the sound file back from the file system in order to play it, and I worked out how to store the file in the applications file space rather than within the sd card.  I am now storing the file in a sub-directory of the application space and reloading in an activity context like this (excluding all the exception handling):

String filename = "test.3gpp";
File dir = this.getDir("sounds", MODE_WORLD_READABLE);
File file = new File(dir, filename);
FileInputStream is = new FileInputStream(file);

MediaPlayer mMediaPlayer = new MediaPlayer();

mMediaPlayer.setDataSource(fileDescriptor);

mMediaPlayer.prepare();

mMediaPlayer.start();

So that was pleasantly simple.

Android Multipart upload

So there were many posts about multipart upload in the Android Google Groups:
One guy rolled his own using HttpURLConnection, and another which strangely relied on dependencies from two different versions of the Apache HttpClient project (commons-httpclient.jar an httpcomponents-client-4.0-alpha4.); since version 3 of Apache's HttpClient was still a commons project, and the latter dependency is a version 4 alpha. The sample code contained no import statements, so it was difficult to know which version of the library was being used for what.

My confusion was deepened when I found a discussion in an Apache forum suggesting that the multipart support had not been included in the move from version 3 to version 4. There was yet another Google Groups discussion on the multipart upload using a different set of Apache libraries. I also found a discussion of the relative merits of using HttpClient versus UrlConnection in Android. I had heard before that some people have experienced odd issues with UrlConnection, which is why I switched over to HttpClient a while back (actually using an AndroidHttpClient class from the zxing project). It is not clear if the URLConnection issues have been resolved with latest code release. Either which way it is somewhat confusing that while there is some of the Apache HttpClient code in Android by default, it is not all there, or at least some of the latest parts of the library are not.

I ended up downloading the latest version of the Android HttpClient library which appeared to be version 4 beta 2 and was pleasantly surprised to find a code sample for a multi-part upload, despite the suggestion I had found that multi-part uploads had not been ported into this version. I modified this example slightly for Android and I was even more pleasantly surprised when it worked (abridged code below):

  
HttpClient client = new DefaultHttpClient();  
HttpPost post = new HttpPost("http://api.whatever.com/something");  
FileBody bin = new FileBody(file);  

StringBody media_entity_part = new StringBody(media_entity); 
 
MultipartEntity reqEntity = new MultipartEntity();  
reqEntity.addPart("bin", bin);  
reqEntity.addPart("media_entity", media_entity_part);  

post.setEntity(reqEntity);  HttpResponse response = client.execute(post);  

HttpEntity resEntity = response.getEntity();  
if (resEntity != null) {    
  resEntity.consumeContent();  
}

The dependencies I had were the jar files that came with the latest HttpClient download:

apache-mime4j-0.5.jar
httpclient-4.0-beta2.jar
httpcore-4.0-beta3.jar
httpmime-4.0-beta2.jar

Of course I may have some overlap there too, but when it starts working you don't ask questions - at least while there doesn't seem to be a critical space issue for my android project at present.

Two feelings I come away with are:

1. Is the StringBody, FileBody class really necessary? Couldn't we just have reqEntity.addFilePart and reqEntity.addStringPart and avoid the extra line and class definitions? Seems like that just doesn't need to be seen by the API user.
2. The discussion of all this multi-part upload stuff is really scattered over the google groups for Android. I can't help but think they might get better convergence if the discussion was organised about the API itself, e.g. that questions about multi-part uploads all appeared linked with the Android docs: http://developer.android.com/reference/org/apache/http/client/HttpClient.html
MySQL, PHP and others have great embedded discussions in their docs - I don't know why Java and Ruby projects can't have similar ...

McClellan (2000) Experience Design

So I am reading this paper as part of researching design for spaces in Second Life. It was recommended to me by Peter Leong who is teaching a course in Second Life this summer for the College of Education at the University of Hawaii.

Interestingly the paper draws together strands of thought from theatre, marketing, economics and a number of other areas. Mclellan talks about how "we" are moving from a service economy to an experience economy as exemplified by organizations such as Disney. Although given the recent economic downtown I start to wonder whether much of economics is illusory :-) I think Mclellan's point is about the importance of companies focusing on the all round experience they create for their customers, but it sits oddly against opinion pieces I have read about how Disney's concept of flying people round the world to experience theme-parks is something we need to move away from given current environmental pressures. Of course that shouldn't really undermine the general point about how important customer experience is, but when Mclellan quotes Wolf (1999) as saying:

that all businesses (even banks and supermarkets) will increasingly need to be entertaining to thrive

I can't help but feel that in banks the focus on customer experience in the last few years may have been at the expense on focusing on making sure the core business is sound. Anyhow, I am being tough on a paper written in 2000 that also points out that customer experience is no substitute for underlying quality.

I am often tempted to be skeptical of papers talking about design concepts but the experience realms diagram adapted from Pine and Gilmore (1999) made some sense to me (see figure above). I am often struck in the difference between active and passive participation in media - characterized by my nightly decisions between playing computer games and watching internet tv. Not sure about their absorption-immersion dimension, but either way apparently any activity can flit between the four categories created by these two dimensions (Entertainment, Esthetic, Educational, Escapist) and I start to feel that the importance of conceptual frameworks like this is not necessarily their perfect mapping to everyone's experience of reality, but the extent to which they help you step outside your usual flow to consider alternate options.

Talking of flow, there is apparently a link between Pine and Gilmore's model and Csikszentmihalyi's (1991, 1997) concept of flow; that strange almost loss of self that one can achieve when really involved in doing something. I connect that with the suspension of belief in novels and stories (see my previous post) and interestingly Mclellan goes on to describe the importance of stories in designing experiences. Apparently AI expert Roger Schank (1991, 1995) theorizes that human thinking depends a great deal on storytelling and story understanding. It makes me start to think about the relationship between stories and memory; and the extent to which information embedded in a knowledge framework gets remembered longer.

From stories Mclellan goes on to talk about drama and theatre and there is a quote from Canadian writer Robertson Davies (1989), who talks about how theatre is an interactive process between actors and audience and can achieve something that tv and movies cannot. I had very similar thoughts when I was involved in the theatre; there was no guarantee that this virtuous cycle of audience reaction stimulating acting prowess and vice versa would necessarily happen, and indeed it seemed to be the exception rather than the norm. However the fact that it could happen was what gave the theatre something solitary use media could never achieve.

A couple of sections of the paper seemed pretty trite to me, i.e. those regarding experience design at the interface, and persuasive technologies; but since those are more my field I guess it is natural that they would seem a little shallow, and I guess in 2000 there might well have been some people who thought that interface design was a doddle - I guess there still are. Anyhow, there were lots of stimulating ideas in this paper; a final example of which was the "third place" concept, of social spaces separate from home and work where individuals could have community interactions.

However I think the interactive drama, passive storytelling dialectic? conflict? paradox? is the key thing here. Why is it so difficult to create an interactive system that is as compelling as some passively-received stories. It is certainly not simple to make engaging stories in passive media (books, tv, film), but the good ones really do grab you and hold you in their world. It is very rare that I can get similarly immersed in an interactive experience - should we be trying for that? Does education only really happen in the interactive mode? Am I learning something about human dynamics from watching Lost? Would I be learning more if I was interacting more with a group of individuals in Second Life? Interactive games generally seem shallow because of the lack of deep human interaction. Most media I find engaging involves stories with deep human interaction. Is the expectation of being interactive versus being passive key? In the 'choose your own adventure' books the suspension of disbelief was often broken by being forced from one mode to another ... can we really sculpt a genuinely satisfying, entertaining educational experience in Second Life? Makes me think about the learning benefits of observing others negotiating meaning (Newton, 1995). I wonder if anyone is putting on theatrical performances in Second Life? Makes me think of the Arthurian quests run by the British Council in teen Second Life.

A good story grabs you through suspension of disbelief which then gives it power to make you think rather than focus on the inconsistencies that break the suspension. One can imagine creating a computer game like experience in Second Life, such as an Arthurian quest, with scripted agents to play roles of different characters, but the human interaction would be rather shallow. I guess that what a virtual world like second life may offer is the ability to create an experience somewhat like that of the actor troupe in Neal Stephenson's the Diamond Age. A couple of teaching assistants could play multiple different roles. I guess I'm imagining a bounded area within which to have a quest that multiple 'learners' would participate in. Except that people could assume different characters so maybe one wouldn't be sure who was who. Maybe that's not so important. I guess the roleplay aspect of it might be the most interesting. We saw some of that in an online language learning class. I guess the trick is to structure the space appropriately, work out the pedagogical goals. Something like the ecological detective stories I saw at AIED would be really interesting in Second Life given that different characters could easily be played by real people .... Can't find the reference to the detective story thing, but other examples of role play from that field are Johnson (2007) and work at North Carolina State. Challenge in Second Life is that there is not much in the way of facial animation - these last two examples are focused on interaction and emotion. Might more easily create detective story environments in SL - populating them with real interesting people perhaps more of a resource challenge ... (could be paying people to be SL actors?)

Cited by 16 [ATGSATOP]

My references

Newton, J. (1995). Task-based interaction and incidental vocabulary learning: a case study. Second Language Research , 11 (2), 159-176.
Johnson, W. L. (2007) Serious Use of a Serious Game for Language Learning. In Proceedings of AIED 2007,
Marina del Rey, CA: IOS Press

Mclellan's references

1. Seybold, P. (1998). Customers.com. New York: Times Business.
2. McLuhan, M. (1967). The medium is the massage: an inventory of effects (Cited by 44). New York: Bantam.
3. Pine, B.J., III, & Gilmore, J.H. (1999). The experience economy: Work is theater and every business a stage. Cambridge, MA: Harvard Business School.
4. Csikszentmihalyi, M. (1991). Flow: The psychology of optimal experience (Cited by 4887). New York: Harper and Row.
5. Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life (Cited by 630). New York: Basic Books.
6. Murray, J. (1997). Hamlet on the holodeck (Cited by 1073). Cambridge, MA: MIT Press.
7. Kim, A. J. (1998). Killers have more fun (Cited by 9). Wired, 6(5):140 –144.
8. Wolf, M.J. (1999). The entertainment economy: How mega-media trends are changing (Cited by 16). New York: Times Books.
9. Jensen, R. (1999). The dream society (Cited by 120). New York: Random House.
10. Atchley, D. (1999). Digital storytelling . Presented at the Consumer Electronic Show, January 8, 1999, Las Vegas, Nevada.
11. Pink, D.H. (1999, January). What’s your story? Fast Company, 21:32–34.
12. Knapp, E. (1999, January 8). Digital storytelling at Pricewaterhouse Coopers. Presentation at the Consumer Electronics Show, Las Vegas, NV.
13. Brown, J.S., Collins, A., & Duguid, S. (1989). Situated cognition and the culture of learning (Cited by 5933). Educational Researcher, 18(1):32–42.
14. Schank, R. (1991). Tell me a story: A new look at real and artificial memory (Cited by 303). New York: Scribners.
15. Schank, R. (1995). Virtual learning: A revolutionary approach to building a highly skilled workforce (Cited by 189). New York: McGraw Hill.
16. Gardner, H. (1995). Leading minds: An anatomy of leadership (Cited by 585). New York: Basic Books.
17. Laurel, B. (1991). Computers as theater (Cited by 217) . Reading, MA: Addison-Wesley.
18. Davies, R. (1989). The lyre of Orpheus (Cited by 12). New York: Penguin Books.
19. Fogg, B.J. (1999, May). Persuasive technologies. Communications of the ACM, pp. 17–19.
20. King, P., & Tester, J. (1999). The landscape of persuasive technologies (Cited by 39). Communication of the ACM 42(5): 31–38.
21. Oldenberg, R. (1997). The great good place: Cafes, coffee shops, community centers, beauty parlors, general stores, bars, hangouts, and how they get you through the day (Cited by 289) (Cited by 289). New York: Marlowe & Company.
22. Oldenberg, R. (1999). The great good place: Cafes, coffee shops, community centers, beauty parlors, general stores, bars, hangouts, and how they get you through the day (Cited by 289) (Cited by 289).New York: Marlowe & Company.
23. Reeves, B., & Nass, C. (1996). The media equation (Cited by 1654). Cambridge,UK: Cambridge University Press.
24. Mok, C. (1996). Designing business. San Jose, CA:Adobe Press.