ADICIONALES DE REMUNERACIÓN

As we have seen in the question 2, different classification systems of lecture discourse are available. Nevertheless, the classification of discourse is carried out at different levels, mainly at episode level (i.e. Mortimer and Scott, 2003) and at lecture level (i.e. MICASE manual, 2002). An episode is a collection of exchanges, or sometimes a single exchange also makes an episode as explained above. At the same time, the classification of episodes and lectures are interconnected. One cannot classify the lecture discourse without identifying the type of interactional episodes, and their duration, etc. Only based on the predominant type of interactional episodes can one classify the lecture discourse as in the case of MICASE. Therefore, the interactional episodes in this study are classified based on the questions that initiated the episodes. Therefore, there are episodes initiated with CDQ, KTQ, KAQ or CMQ and similarly the episodes are identified as Concept Development Episodes (CDE), Knowledge Testing Episodes (KTE), Knowledge Application Episodes (KAE) or Classroom Management Episodes (CME). I will revisit these episodes after discussing the overall system.

The system to classify the overall discourse pattern of lectures borrows ideas from Mortimer and Scott’s (2003), and the MICASE classifications. A detailed discussion of these studies was given earlier. Mortimer and Scott (2003) classify interactional episodes into dialogic/authoritative and interactive/non-interactive categories. Even

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though the latter classification is widely used in studies (e.g. Scott and Mortimer, 2005; Mortimer et al. 2006; Scott and Ametller, 2007) its application is possible at episode level only, but not at lecture level. Moreover, as explained previously in question 2, the category called ‘dialogic/non-interactive’, as defined by Mortimer and Scott, is a confusing one. In addition, all these four categories are mutually exclusive as they are meant for interactional episodes, whereas a lecture might posses the features of two or more categories without fitting exclusively into one of these categories, because a lecture is a collection of different interactive/non interactive, or monologic/dialogic segments or episodes. For example, certain parts of the lecture may be monologic (i.e. summary), while others could be dialogic. Therefore, a classification system that is able to cover these differences is needed. The features of their classification system that could be borrowed for the proposed system are the dialogic /authoritative (monologic) divisions and the way they define the episodes. In addition, another advantage of their system is the argument that a lecture can be interactive but could still be authoritative or non-dialogic as called in this study. This concept can also be incorporated into the system which I propose.

MICASE (2002)

Mortimer and Scott’s (2003)

Figure 5.1: Comparison of MICASE and Mortimer and Scott’s discourse classifications

The classification system presented in the MICASE manual (2002), as we saw earlier, identifies interactive and monologic categories in speech events. In addition, the MICASE classification is applicable to whole lectures and it is similar to Mortimer

Category Highly monologic Mostly monologic Mixed Mostly interactive Highly interactive Interactive/dialogic Non-interactive/dialogic Interactive/authoritative Non-interactive/authoritative

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and Scott’s interactive and non-interactive categories, but it does not consider the dialogic/authoritative categories, like Mortimer and Scott. The MICASE manual (2002) classification and Mortimer and Scott’s (2003) appear above side by side in figure 5.1.

In MICASE there is a category called Mixed so that when a lecture shows both features of interactive and monologic it can be placed in the middle. At one end of its continuum it has monologic, while at the other end it has interactive on the assumption that interactive is the polar opposite of monologic. In addition, different categories are structured along a continuum so that a lecture can be fitted into one of these categories.

Even though MICASE uses interactive and monologic categories, the MICASE category ‘interactive’ would not address the purpose of the current research. That is, to identify the lectures that could promote dialogic teaching through students’ contribution towards knowledge building. In addition, a lecture can be interactive with many KTQs but without any being useful for either knowledge building or giving enough opportunities for students’ to produce output. This discourse is similar to the interactive/authoritative category of Mortimer and Scott (2003). Opportunities to produce output is important as Swain (1995) argues that output is believed to assist second language acquisition, as explained in the literature review chapter (section 3.7). Further, MICASE has not considered the dialogic value of the discourse, while dialogic is an important category under Mortimer and Scott’s system.

Another problem with MICASE is the operationalisation of the MICASE categories. For example, one of the MICASE categories ‘mostly monologic’ is defined as ‘a lecture which is primarily monologic discourse interspersed with some segments of

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interactive discourse’. Here the word ‘some’ is an abstract property and it cannot be easily quantified looking at a lecture discourse. That is, ‘some’ does not describe what number or what duration. This confusion is applicable to other categories of MICASE too. Therefore, though features of MICASE seem to be a suitable classification system for the present lecture discourse, there are two kinds of inherent problems if one is to use the MICASE system. One is the lack of accountability of the dialogic discourse and the other one is the difficulty in the operationalisation of the MICASE categories. Hence, a slightly modified system would overcome these two practical difficulties and could yield a system that is suitable for the classification of overall lecture discourse at FAS.

In order to overcome the problem of the operationalisation of the MICASE categories, the total duration of interactional episodes could be considered. At the beginning the total duration of interactional episodes would be a suitable indicator to mark the extent to which the lecturers and students interact with each other. This is preferable to a classification system that uses the number of questions asked (e.g. Morell, 2004) because the number does not give a clear idea of the intensity of the interaction which takes place. In addition, to operationalise the MICASE categories some assertions are needed. The duration of the lecture hour at FAS is one hour and the total average talking time of the 12 transcribed lectures is 53 minutes. Considering 50 as the maximum duration of interaction that would possibly take place in a single lecture, five interactive categories could be assigned with five scales as shown below in table 5.3.

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Table 5.3: Classification system for the FAS overall lecture discourse MICASE categories Value assigned with total duration

of interactional episodes (minutes)

Highly Monologic < 10 Mostly Monologic 10–20 Mixed Lectures 21–30 Mostly Interactive 31–40 Highly Interactive > 40

Now based on the duration of the total interactional episodes of each lecture its overall discourse type could easily be decided. For example, if a lecture’s total duration of interactional episodes is 15 minutes it can be classified as mostly monologic. However, now attention should also be paid to the second problem with MICASE – lack of dialogic value of the discourse. In order to consider the dialogic values within interactive lectures, a conditional sub category could be made within interactive lectures. The basic consideration is that for a lecture to be dialogic it should have interactional episodes of the concept development category. Concept development episodes (CDE), in comparison, have the potential to incorporate the students’ views into knowledge building, although others too have some other communicative values such as a KAE. Therefore, a lecture which is mostly or highly interactive has a substantial amount of lecturer-student interaction. In addition, if the CDE predominates in the total interactional episodes, those lectures could be classified as dialogic or else they are interactive (or non-dialogic) only.

The new system developed for the current study has categories, similar to MICASE, from monologic to dialogic lectures and in between a category called mixed lectures. In addition, it has two more categories within the dialogic lectures: mostly dialogic and highly dialogic. These two would be alternative categories to both mostly interactive and highly interactive respectively. If a highly interactive lecture contains a higher duration of CDEs over other episodes, it is called highly dialogic. Similarly,

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mostly interactive lectures as mostly dialogic, when CDEs predominate in their discourse, as shown in table 5.4 below.

Table 5.4: Classification system for the FAS overall lecture discourse Duration of total interactional episodes

(minutes) Discourse category of lecture21 <10 Highly Monologic 10–20 Mostly Monologic 21–30 Mixed Lectures

31–40 Other types of episodes predominates the discourse

Mostly Interactive CDE predominates the discourse Mostly Dialogic > 40 Other types of episodes

predominates the discourse

Highly Interactive CDE predominates the discourse Highly Dialogic

As a summary, the proposed system, similar to MICASE, has five categories: highly monologic, mostly monologic, mixed, mostly interactive and highly interactive. However, unlike MICASE, this study considers the dialogic teaching also as a key feature for successful lecture delivery and therefore, within the interactive categories it introduces two alternative categories. They are mostly dialogic and highly dialogic. In addition, they are decided based on the presence of CDE and other types of episodes, i.e. knowledge testing episodes (KTE), knowledge application episodes (KAE) or classroom management episodes (CME).

Even though in other types of episodes also, other than the dialogic, there are interactional exchanges the benefits of such interaction towards comprehension and language development is in question. For example, a knowledge testing question which leads to KTE makes students respond in one or two words and might not place a cognitive demand on students. Rather it tests students’ memory and also most of the time it ends as one to one recitation script. On the other hand, the presence of CDEs

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 In this category monologic refers to non-interactive discourse, while interactive refers to non- dialogic discourse.

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cannot be assumed to always bring lengthy answers from students, though using CDEs one can generate extended sequence of interaction, when other conditions are favourable (e.g. cooperation from students). The proposed classification system for the FAS discourse containing the three important discourse components is given below in table 5.5.

Table 5.5: The composite discourse components and their classification system Discourse components Classification system

Lecturer’s Questions Concept development questions; Knowledge testing question; knowledge application question and Classroom management questions

Pattern of Interactional episode

IRF/or their variants (IRFRF or IRPRPR) Type of overall lecture

discourse

Highly monologic; Mostly monologic; Mixed lectures; Mostly interactive /mostly dialogic; Highly interactive/highly dialogic

5.6 Chapter summary

In this chapter a review has been undertaken with a view to designing an analytical system for lecture discourse collected for the FAS discourse. The system developed to analyse the discourse contained three components as shown in the above table. In this review the relative importance of each of those components and a rationale for the system developed was also explained. Though this classification system has been developed for the FAS lectures, it is not necessarily limited to FAS and can be extended to other faculties of the university where the medium of instruction is English and also to other universities which have a similar situation to FAS, as I discuss in the discussion chapter. In addition, the data analysed using this analytical system can give more in-depth insight into the data collected through survey and interview.

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CHAPTER 6 – FINDINGS: From