Reading comprehension exercises online: the effects of feedback, proficiency and interaction.

When considering the quality of interaction around computers, two key features are particularly desirable: (1) learners need to be actively involved (Van den Branden, 2000); and (2) learners need to produce Exploratory talk in which partners engage critically and constructively with each other's ideas (Mercer, 1995). Regarding the former, Mercer (2004) explains how it is helpful for the analyst to perceive the degree in which students in joint activities are: "(a) behaving cooperatively or competitively and (b) engaging in the critical reflection or in the mutual acceptance of ideas" (p. 146). As for promoting Exploratory talk among learners, Wegerif, Mercer, & Dawes (1998), having been influenced by findings of research into effective collaborative learning (summarized in Wegerif & Mercer, 1996), note the importance of: sharing relevant information, reaching agreement, expecting reasons and challenges, discussing alternatives and encouraging peers. A key concern for research, however, is how quality interaction and reading comprehension can be promoted through computer-based activities.

Reading, Computers and the Internet

Despite the fact that models and guidelines recommending pedagogically sound practices for incorporating Internet-based materials exist (Berry, 2000; Brandl, 2002; Chun & Plass, 2000), a major concern is that the number of such examples remains limited. Likewise, guidelines for offering a reading course via the Internet (Caverly & McConald, 1998; Jones & Wolf, 2001; Mikulecky, 1998) are similarly few. However, evidence exists to support the assumption that integrating reading with computer-mediated support improves ESL students' reading skills (Chun & Plass, 1996; Hong, 1997; Stakhnevich, 2002; Williams & Williams, 2000). A common theme in studies such as these is that learners benefit from facilities offering support and assistance in web learning environments, for instance, from online dictionaries, glosses, graphics, blogs, bulletin boards or chat rooms. As a further example of a potentially advantageous facility that can also be offered through computer-assisted language learning (CALL), the following section comprises a discussion of the merits of computer-mediated feedback.

COMPUTER-MEDIATED FEEDBACK

One area in which computers are playing an increasingly important role in SLA concerns the identification of students' errors and the subsequent provision of appropriate feedback (Brandl, 1995; Clark & Dwyer, 1998; Tsutsui, 2004). However, many software products opt for a generic form of feedback and rarely go above the level of indicating whether an answer to a question is correct or incorrect (Sales, 1993). Beatty (2003) explains:

A CALL program is likely implicitly to state

'I have the answers to your questions; just

click here.' A teacher is more likely to say,

'What do you think the answer might be?' or

'Why do you ask this question?' ... it is

difficult for computers to deal with ambiguous

learner input, but this is an area of research

that needs to be further investigated. (p. 138)

Clariana (2000), who has published extensively on the topics of computer-mediated feedback, provides a succinct summary of the traditionally investigated types of feedback in CALL:

Knowledge of response (KR) that states "right" or "wrong" or otherwise tells learners whether their response is correct or incorrect; Knowledge of correct response (KCR) that states or indicates the correct response; and Elaborative feedback that includes several more complex forms of feedback that explains, directs, or monitors (Smith, 1988). Elaborative feedback includes the forms listed below:

1) Explanatory feedback provides additional explanations, such as why a learner's error response is incorrect or perhaps why a correct response is correct and various types of additional remedial screens that may amount to new instruction (Merrill, 1985, 1987; Spock, 1987).

2) Directive feedback may provide prompts, hints, or cues to assist the learner in determining the correct response (Nielson, 1990). Answer until correct (AUC, Pressey, 1926) is a common form of elaborative feedback where the learner is directed to respond until correct.

3) Monitoring feedback, also referred to as advisement, lets the learner know how they are doing overall. (p.1)

As can be seen, several forms of feedback exist. Accordingly, numerous researchers have tried to identify the most effective forms in different contexts. This issue is addressed in the next section.

How Effective is Feedback?

It is difficult to say which type of feedback is best for SLA as results are mixed (Clariana, 2000; Mory 1994). For example, summarizing findings by Bangert-Drowns, Kulik, Kulik, and Morgan (1991), AUC and Elaborative feedback are considered to be the most effective:

KR < no feedback < KCR < AUC = Elaborative feedback

However, following a review of 30 studies, Clariana's (1993) findings, which are consistent with both Schimmel's (1983) meta-analysis of 15 studies and also Kulhavy and Wager's (1993) research, show feedback has proven more effective than no feedback:

No feedback < KR = KCR = Directive feedback = Multiple try feedback

(AUC)

In addition to investigating the most effective type of feedback, various other aspects of computermediated feedback have also been the focus of research. For example:

1) Clariana and Koul (2006) investigate the effects of multiple-try immediate feedback for questions differing in difficulty

2) Clariana and Koul (2005) compare multiple-try feedback to other types of feedback also for questions of differing difficulty

3) Clariana (2003) and Clariana and Lee (2001) investigate the effects of recognition (multiple-choice) and recall (constructed-response) study tasks with feedback

4) Clariana, Wagner and Murphy (2000) consider the timing of feedback and

5) Nagata (1996) and Mory (1994) investigate the effects of Elaborative feedback.

A key question is whether computer-based feedback can offer advantages over traditional paper-based answer papers. Of particular interest, therefore, is the fact that Nagata (1996) found ongoing intelligent computer feedback to be more effective than simple workbook answer sheets for developing grammatical skill in producing Japanese particles and sentences. However, Clariana (2000, p. 2) draws on research to show how, in contrast to Nagata (1996) and Bangert-Drowns et al. (1991), Elaborative forms of feedback often produce no significant improvement over KR feedback despite requiring considerable development and implementation cost (Merrill, 1987). Nevertheless, Ferris (2003) explains how indirect feedback, or Elaborative feedback from a CALL perspective, is generally thought to be conducive to long-term student development; it forces students to think about their own errors and self-correction, thereby leading to: " ... increased student engagement and attention to forms and problems" (p. 52). Indeed, de Bot (1996) explains how students need to be active when producing language to discover what they can and cannot do. Noticing a problem, possibly through feedback, may be the incentive learners need to reengage with information in the input, thereby providing an opportunity for learning. Therefore, by placing the onus on the students to identify and correct their own errors, it would seem that the potential for interaction and negotiation of meaning, form and / or content is increased.

THE NEED FOR RESEARCH

When providing feedback, what messages should be supplied to the students on the screen? If feedback is presented in the form of KR or KCR feedback, it is simple to envisage what is displayed (a message such as right or wrong, a highlighted answer or a mark indicating the correct answer); however, with Elaborative feedback, what is it exactly that should be displayed to promote interaction and comprehension? This is the crucial question for anyone creating software to provide such feedback. Among the extensive literature related to the theme of feedback, the number of studies researching Elaborative feedback is relatively small; however, notable examples exist (Brandl, 1995; Clark and Dwyer, 1998; Mory, 1994; Nagata, 1996; Van der Linden, 1993). Even more difficult to find are examples of guidelines for its presentation. For example, Van der Linden (1993) notes: "Long feedback (exceeding three lines) is not read and for that reason is not useful" (p. 56). Van der Linden concludes that: "... feedback, in order to be consulted, has to be concise and precise." Mory (1994) recommends that isolated feedback should be avoided as it may provide little context for revision of an erroneous response. Consequently, Mory advises designers to: "... include the learner's answer and other alternative choices on the same screen as the feedback" (p. 287). As Chapelle (2001) explains, therefore, further research is vital: "What is needed are theoretically and empirically based criteria for choosing among the potential design options and methods for evaluating their effectiveness for promoting learners' communicative L2 ability" (p. 2). Accordingly, the following study comprises an investigation into the effectiveness of Elaborative feedback with the aim of identifying guidelines for researchers creating software to provide such feedback.

Research hypotheses