||Name of the system or application
||Publications studying/presenting this system
||First year of publication for related papers
||Primary modality of learner production (user input) in the system
||Type of system as per the proposed dialogue-based CALL typology (see 4.2. A typology of dialogues and systems)
||Narrative, Form-focused, Goal-oriented, Reactive
||Type of instructional dialogue as per the proposed dialogue-based CALL typology (see 4.2. A typology of dialogues and systems)
||Branching, Form-focused exercises, Elicited, Meaning-constrained, Form-constrained, Contextualized, Free
||Constraints imposed on the form of user production
||limited syntax, list of correct and incorrect utterances, list of utterances w diff meanings, list of words, none, rearrange blocks, rearrange blocks, gap-filling, use target structures, verbatim prompt
||Constraints imposed on the meaning of user production
||context, cues, determined answer, determined answers, determined answers to be translated, elicitation sentences, given, instructions, list of utterances, list of utterances w same meaning, meaning is irrelevant, next turn set, none, physical context, questions, system-init., task, text to be translated, visual context
||Type of formal constraints
||pre-set, explicit, none
||Type of meaning/semantic constraints
||pre-set, explicit, implicit, none
||Type of corrective feedback provided by the system on learner production
||Explicit, Explicit (on pronunciation), Implicit, Implicit (recast), No
||Target language (L2) to be practiced in the system
||Management of initiative in the dialogue (who controls the dialogue flow)
||System, System + mixed, User, User + mixed
||Does the interaction occurs in a virtual world (visually represented or not)?
||Has the system agent an embodied representation (e.g., avatar)?
||Is the system implemented into a virtual robot?
||Is the system gamified in any way (e.g., emphasis on scoring and competition, implementation of gaming logics)?
||Does the system implement user modelling or user adaptivity?
||Eventual dialogue system/chatbot framework used to develop the system
||Type of dialogue modelling
||branching, fixed path, frame, graph, matching, n/a, plan, plan / branching (?), probabilistic matching, probabilistic matching (levenstein distance with corpus utterances)
||Type of interaction
||goal-oriented, open-ended, system-guided
||Is the application built on a (handcrafted) rules-driven or (probabilistic) data-driven dialogue system?
||Corpus-based, Dialogue models derived from corpus, Handcrafted rules, Handcrafted rules (mostly), Learning from previous interactions, Rules learned from literary corpus, Rules learned from SCMC corpus
||Steps of processing for the user utterance for managing the dialogue
||Depth of natural language processing (i.e., does it go beyond surface processing/pattern matching and try to extract meaning?)
||none, shallow, medium, deep, NA
||Breadth of natural language processing (i.e., does it expect a limited number of possible utterances/words, from a very specific domain, or is it very extensive in the possible inputs that are considered?)
||none, narrow, medium, broad, NA
||Strand/field of publication, based on the main references cited, main keywords used and orientation of the content (see 3.3 and 3.4)
||Chatbots, SDS/CA, CAPT, ICALL
||Abbreviated name of the system (used in figures)
||Is this system available to the general public?
||URL of web page where the system is located or presented in details