InterLect - Lecture Content Interface

Robin Nicolay

, Bastian Schwennigcke

, Jonas Vetterick

Wolfgang Sucharowski

and Clemens H. Cap

Department of Computer Science, University of Rostock, Rostock, Germany

Department of Humanities, University of Rostock, Rostock, Germany

Keywords:

mLearning, Content Interface, Digital Teaching Content.

Abstract:

Large audiences complicate interactions between lecturer and individual students. Because students differ in

their individual qualities, all of them have their own individual needs and challenges - at different topics and

at different points in time. We reviewed current academic teaching processes in their way of presenting and

consuming lecture material. Based on our ﬁndings, we implemented InterLect to observe students’ perception

of lecture material and to examine their conﬂicts and needs during the lecture. These needs help us to improve

the awareness on comprehension and determination of appropriate learning activities, so students are able to

increase their beneﬁt from the teaching content.

1 INTRODUCTION:

ENCOURAGING THE “DEEP

APPROACH” ON LEARNING

As the educational researcher John Biggs suggests,

passing through academic studies is a way to alter an

individuals interactions with the world. Biggs states

(Biggs, 1999, p. 60), “as we learn, our conceptions of

phenomena change, and we see the world differently.”

Thus, learning should not be dominantly about the

quantity of information a learner is able to store and

recall. It is about whether a learner is able to “struc-

ture that information and think with it [...].” This level

of information processing may only be attained by a

“deep approach on learning”, where learners need to

go beyond “memorizing” and “note taking”. (Biggs,

1999, p. 59f) They will have to expand their engage-

ment in information processing to the description, ex-

planation and interrelation of phenomena. They will

have to apply and theorize problem solutions.

Consequences on the design of teaching are far

reaching. It will not be sufﬁcient to anticipate the

degree of knowledge and ability on the students side

in order to adjust the extent and vividness of infor-

mation which is supplied in a lecture lesson. Teach-

ing should rather encourage and develop the students’

strategies to combine, link, reduce and enhance infor-

mation within the backdrop of their individual epis-

temic processes. Furthermore, teaching should help

to reveal and change the implications that conduct

such processes (Biggs, 1999, p. 63).

Consequently, encouraging the deep approach on

learning will change the way lectures work. They will

cease to be just successions of propositions and illus-

trations but instead help to establish spaces of action,

where students are challenged to progressively build

up, evaluate and develop conceptual frameworks en-

abling them to acquire epistemic objects. Section 2

discusses identiﬁed issues and possible enhancements

in the process of content delivery and information

processing.

Current Classroom Response systems provide en-

vironmental and mobile tools to reform the level of

interaction between lecturer and audience. These sys-

tems focus on improving the process of teaching by

encouraging interaction between lecturer and audi-

ence. Besides improvements of Classroom Response

Systems as described in section 3, we have to con-

sider the fast evolution of mobile phones as well as an

increasing availability on mobile devices among stu-

dents (Hanley, 2013). This development enables a use

of private devices to interact with delivered informa-

tion in lecture scenarios. Used as an interactive visual

channel, private devices support a high level of indi-

viduality in ways of processing information as well

as expressing requirements. Improvements in com-

parison to the current state of the art in Classroom

Response Systems are discussed in section 3.

Since the delivered information correlates with the

269

Nicolay R., Schwennigcke B., Vetterick J., Sucharowski W. and H. Cap C..

InterLect - Lecture Content Interface.

DOI: 10.5220/0005440102690276

In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 269-276

ISBN: 978-989-758-107-6

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

presented slides, we conclude, students’ conﬂicts in

comprehension of lecture information should be se-

mantically attached to slide material itself.

Therefore, we aim to identify and resolve stu-

dents’ conﬂicts in processing information induced by

lecture material such as misconceptions (Lucariello,

2015) or further difﬁculties in comprehension, during

and after a lecture. Section 4 introduces InterLect as a

new evaluation prototype that connects students to the

lecture material based on ﬁndings of section 2. The

prototype is able to identify and evaluate students’

conﬂicts based on lecture information. Evaluated pa-

rameters and expected observations are discussed in

5. Summarizing, InterLect enables a collection of in-

sights into student’s reﬂection processes to determine

ways in assisting conﬂict recognition and handling,

for both the student and the lecturer side.

2 DIDACTICAL ISSUES

In this section we discuss a dynamic approach on

lecture reception. It emphasizes learners’ activities

while attending lectures from a situated learning the-

ory approach and shall outline basic principles for the

design and evaluation of the learning processes in-

duced by a lecture content interface.

2.1 ’Doing’ Lecture - A Generative

Approach on Lecture Reception

Learning goes beyond transfer and documentation of

content within a lesson. Learning concerns all pro-

cesses a system works out to adopt to the affordances

of any environment relevant to this system. In partic-

ular, academic learning has to establish learning en-

vironments where problem solving has to be rational-

ized and has to detect epistemic objects which are rel-

evant against the background of scientiﬁc discourse.

Lectures only play a decisive role within these pro-

cesses, if they provide information or hints, which

can be used to focus, improve or revise the adoption

processes of a learning system to such environments

(Biggs, 1999, p. 60) (Laurillard, 2008) (Laurillard,

2012, p. 54-56).

More precisely, lectures perform this function as

part of a sense-making process (Lee et al., 2008)

(Tobias, 2010), which has to be considered as the

core process of knowledge communication. It delib-

erates and - if required - evaluates and develops the

conditions and prerequisites a learner needs in order

to accomplish affordances in learning environments,

i.e. writing a paper, preparing talks and exams, car-

rying out exercises and so forth. From this point

of view, lectures are no means just to transmit ab-

stract information. In fact lectures are part of a dy-

namic interplay that coordinates affordances of sit-

uated learning with processes that externalize, order

and revise the conditions of accomplishment within

learning environments (Laurillard, 2012, p. 116-121)

(van Merri

enboer and Kester, 2014).

This interplay has to be conceived as an active and

generative practice of searching for inter-relations be-

tween situated constraints on the learners side and ex-

ternal stimuli from a university course or other media

of teaching. It is about building focus and empha-

sizing areas of interest within given lecture material.

Through this interplay between accomplishing situ-

ated learning and following courses, the learner per-

forms a rather praxeological ’doing’ or constructing

lectures as the medial and material backdrop (Latour

and Woolgar, 1986) of his or her individual learning

processes. That means, we question how lectures be-

come part of a students’ ecology of learning’ (Mio-

duser, 2015) that provides external forms and struc-

tures of thinking and acting and how students acquire

such structures by means of active intervention into

lecture materials.

Research on generative learning, established by

Merlin C. Wittrock (Wittrock, 1990) (Wittrock, 1992)

and currently debated by (Lee et al., 2008)(Tobias,

2010)(Leutner and Schmeck, 2014), has found three

different ways of so-called coding, i.e. actively inter-

vening into given educational material, such as lec-

ture slides, with different effects on learning outcome.

Depending on their motivational state, memory and

meta-cognitive skills, learners use simple and com-

plex coding as well as higher integration strategies in

order to search for inter-relationships between new in-

formation and previous knowledge and to derive con-

ceptual or schematic ”knowledge units” (Lee et al.,

2008, p. 113) from new information.

In summary, ”simple coding strategies”, i.e. ”un-

derlining, note taking and adjunct or inserted ques-

tions” (Lee et al., 2008, p. 114) have strong effects on

recall and comprehension of new information. Espe-

cially learners with ”low prior knowledge” (Lee et al.,

2008, p. 115,121) beneﬁt from note-taking, which im-

proves their performance in information recall (Lee

et al., 2008, p. 115) as well as in coping with transfer

tasks (Lee et al., 2008, p. 121). In comparison re-

search on ”complex coding strategies”, i.e. ”the cre-

ation of hierarchies, headings, summaries (etc.)” (Lee

et al., 2008, p. 114) did not result in unambiguous ef-

fects yet (Lee et al., 2008, p. 114f). So-called ”elab-

orative integration strategies”, e.g. ”imaging and cre-

ation of examples, interpretations or analogies” (Lee

et al., 2008, p. 114) are associated with forms of

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

270

”higher order thinking” like ”problem solving, rea-

soning, inference and application” (Lee et al., 2008,

p. 115). Research has shown that these strategies

depend on exercise and time of experience. Espe-

cially, beneﬁting from concept maps seems to require

a higher degree of prior knowledge within the respec-

tively treated domain (Lee et al., 2008, p. 121).

The following section outlines our application

of generative learning strategies for preparing their

technical implementation within a lecture-content-

interface.

2.2 Basic Principles of Generative

Lecture Reception

Using generative strategies while attending lectures

require active and productive reactions on input and

stimulus from a lecture presentation. Those reactions

select and elaborate given input in order to record it

and to rearrange it beyond the sequential order of a

slideshow or at least to produce initial hinds on con-

textual relations. Constrained by the affordances of

speciﬁc learning environments, generative strategies

will select a speciﬁcally rich content form a lecture

presentation. This selection and partial enrichment

of content serves to re-construct and re-structure lec-

ture material in order to constitute conditions of ac-

complishment in learning environments. Generative

strategies support individual consciousness and social

interchange on utilizing lecture or other media mate-

rial for learning processes (Hannaﬁn et al., 2014).

Our efforts on technical implementation of gener-

ative strategies in lecture reception currently concen-

trate on simple strategies, as introduced above. That

means, enabling information enhancement at local

data points of lecture presentation. In particular, we

distinguish three basic principles of generative lecture

reception: The ﬁrst principle is marking. It covers

forms of highlighting content with respect to further

processing. It is a simple activity of content selection,

which is part of any other generative Strategy.

Secondly, we propose linking as generative prin-

ciple. It involves forms of creating inter-relations be-

tween different data points within given stimuli. The

angles of linkage could be semantically qualiﬁed or

not. Linking allows to create data knots as inductively

constituted scopes of relevance as well as sequences

of coherence between data points independent from

succession of slides or presentational concept.

Finally, we assume that identiﬁcation and labeling

of epistemic conﬂicts are as important as generative

principles of lecture reception. Conﬂicting interven-

tions are non afﬁrmative reactions on lecture input.

According to our theoretical assumptions on learning,

they occur when situated learning processes and lec-

ture stimuli do not interlock. The process of sense-

making from a lecture is disturbed. The inter-relation

between learning environment and lecture has to be

readjusted.

3 CLASSROOM RESPONSE

SYSTEMS

Digital tools are been used in classrooms more and

more over the last years. Although there are several

different categories of them, the approach described

in this publication are mainly covered by Classroom

Repsonse Systems (CRS), which focuses on learning-

tools that are used during a lesson.

Traditional Classroom Response Systems have in

the past been known as clickers (Kay and LeSage,

2009). They are utilized to ask multiple choice ques-

tions in lessons with large audiences and therefore re-

quire a device for every participant. Since these de-

vices have to be purchased, handed out, collected af-

ter a lesson, and maintained, they are not widely used.

In the last years several studies have been shown that

the CRS functionality can be perfectly implemented

on mobile devices. Whereas there are applications

that rely on a phone-call ((Dunn et al., 2013)), where

every question gets its own telephone number, Mod-

ern Classroom Response Systems communicate over

an internet connection (Feiten et al., 2012) (Jenk-

ins, 2007) (Kundisch et al., 2012) (Vetterick et al.,

2013). On the one hand there are implementations

that provide native apps for iOS and Android. On the

other hand there are Modern CRS that are accessible

through a responsive web site (scales for any kind of

device). Most Modern CRS enhance their functional-

ity with more ways to provide feedback. For example

some allow students to ask questions in a facebook-

like Chatwall.

CRS in general implement a tool for students, so

that they can be able to provide feedback to their

teachers in real-time during a lecture. Furthermore

modern CRS do the same, but on the devices the stu-

dents already have. Moreover, modern CRS allow ac-

cess to the feedback given in past lessons. Students

use this to prepare for their exams, whereas teach-

ers use it to improve their teaching stuff or style of

teaching. But even with all the feedback-mechanism,

modern CRS lack of a direct link between students

feedback and teachers teaching content and further,

are only able to bring up audience-wide problems.

In some functionalities, as the Quiz for example, the

teachers only see issues if they arise in groups if most

students answer a question correctly, teachers are not

InterLect-LectureContentInterface

271

able to focus on the ones who gave the wrong answer.

The solution presented in the following chapter

overcomes this lack of individual awareness by bridg-

ing the teaching content with conﬂicts arising in stu-

dents’ leaning process. This allows a private learning

assistance that enables students to discover their in-

dividual weakness, while teachers are able to get in-

formed about these conﬂicts.

Due to the challenging task for a lecturer to inter-

act with individuals in large audiences, bridging the

gap between lecturers and their audience strongly de-

pends on the lecturers capabilities. However, our ap-

proach focuses on supporting the individual require-

ments. Therefore, we cannot rely on interaction with

the lecturer, but focus on bridging the gap between

audience and content.

4 THE InterLect SYSTEM

To connect students to a lecture and evaluate ways

of didactic assistance described in 2, we implement a

web based application named ”InterLect”. In its’ cur-

rent phase, InterLect serves as a tool to evaluate stu-

dents’ reﬂection processes. While InterLect is still a

prototype, it supports a quantitative evaluation of per-

formed reﬂection interactions such as marking, link-

ing and conﬂicting. Additionally, it supports a qual-

itative evaluation of performed interactions by auto-

matically generated and distributed questionnaires af-

ter a talk.

The InterLect-Software consists of three parts.

The ﬁrst part is a python webserver (tornado

) for

client hosting and data distribution via web sock-

ets using SockJS

. For storing and maintaining col-

lected data described below, the server uses a Mongo

Database

. Second part is a web client for slide pre-

sentation named “InterLect-Presentation”. The third

part is a mobile web client serving as lecture con-

tent interface named “InterLect-Audience”. All web

clients of InterLect use the Twitter Bootstrap frame-

work (Otto, 2011) to support a responsive layout of

content for desktop systems as well as different types

of mobile devices.

To start a talk the lecturer opens the InterLect-

Presentation web client on an environmental presen-

tation screen. This client displays a connection URL

and a lesson identiﬁer, so that students are able to at-

tach themselves to the lesson via InterLect-Audience

client using their mobile phones. The following sub-

http://www.tornadoweb.org/

https://github.com/sockjs/sockjs-client

http://www.mongodb.org

sections introduce the application side, both the pre-

sentation and the audience client, in detail.

4.1 InterLect-Presentation

To support the process of presenting slides and in-

troduce lecture material into our system, we imple-

mented InterLect-Presentation. We aimed for a max-

imal usability and a minimal effort to lower the entry

threshold of using the prototype.

InterLect-Presentation is implemented as a web

application, shown in ﬁgure 1, and can be opened in

any web browser on any device without installation.

The slide viewer supports slide show material in PDF

format to facilitate the use of already existing lecture

materials. Therefore, PDF ﬁles can be opened di-

rectly from local ﬁle system. After closing the start

dialog a slide is rendered on a full screen HTML5

Canvas using PDFjs (Gal, 2011). To minimize the

impact on common presentation procedures, the han-

dling of InterLect-Presentation is very similar to well-

known presentation tools, such as PowerPoint (Mi-

crosoft, 2014) or Acrobat Reader (Adobe Systems,

2014).

During the presentation, the system transforms

presented slides into a PNG Data-URL image (Hick-

son and WHATWG, 2014) and sends it to the audi-

ence’s mobile phones. Hereby, the presenter has the

option to decide whether a button has to be pressed

to share a slide or slides are shared automatically. In

case of automatic sharing, the presenter can set a min-

imum displaying time before a slide is shared. This

delay prevents automatic slide sharing while scrolling

through lecture material in situations, such as search-

ing for speciﬁc slides or skipping parts of presentation

material.

4.2 InterLect-Audience

To connect the audience with the currently pre-

sented lecture materials, we implemented InterLect-

Audience. InterLect-Audience is implemented as re-

sponsive web client to support a multitude of different

mobile devices and desktop systems. The audience

connects InterLect-Audience to the current lecture us-

ing a lesson ID provided by InterLect-Presentation.

To identify listeners, we use anonymously generated

user IDs, which are stored locally on a user’s device.

To enable personalized user identiﬁcation for a user

on any device, a listener is further able to compile an

anonymous user ID based on its own username and

password. We use user IDs only to recognize indi-

viduals and is not in our intention to enable further

student rating.

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272

Figure 1: InterLect’s starting screen, showed for teachers.

PDF ﬁle can be selected from local ﬁle system. Both con-

nection URL for the audience and lesson ID are displayed

on top of the page.

A successfully connected listener receives all les-

son slides, shared by the lecturer, in a scrollable list

as shown in ﬁgure 2. Every slide can be selected to

open a content dialog shown in ﬁgure 3. The dialog

offers the student a set of interactions to process se-

lected content. Performed interactions, discussed in

the next section, are send to our server and stored in a

database.

Figure 2: Interlect showing an overview of all slides.

5 InterLect’s LECTURE

MATERIAL INTERACTIONS

The content dialog of the audience client shown in 3

allows the audience to interact with the lecture con-

tent. It consists of three major interaction metaphors.

First, marking lecture content with the use of buttons.

Second, placing notes that can be enhanced by hash-

tags and references. And ﬁnally, deﬁning bookmarks

to highlight and quickly reference important slides at

any point in the lecture. All three parts aim to support

basal selection processes, such as marking, linking,

and conﬂicting as described in 2. In this section we in-

troduce these parts and describe their role in the eval-

uation of students’ information reﬂection processes.

Figure 3: InterLect’s content dialog, showed to students

with editable buttons to mark content, a note ﬁeld support-

ing hashtags, and references, and a bookmark input ﬁeld.

As the ﬁrst of three parts, we support a basal mark-

ing of lecture material by a set of conﬁgurable but-

tons. Buttons can be created and edited by the user

themselves. Depending on the type of listener and

presentation we expect to see different approaches of

listeners creations and usage of these buttons.

Students will quickly mark current content for

later processing. We aim to examine created and

used buttons to explore desired classes of reprocess-

ing, such as marking important content for later ex-

ams or the level of interest and comprehension for

later repetition. These markings give us an insight

into intuitive student post-processing techniques.

As another approach, listeners of a lecture might

use these buttons to express conﬂicts as negational re-

actions to current content, such as “Where does this

information belong to?”, “What is the objective of this

information?”, “I need examples”. The observation

of expressed conﬂicts helps us to deﬁne appropriate

ways to enhance lecture slides with semantic infor-

mation, such as annotations and semantic linking be-

tween slides. These enhancements allow a conﬂict

initiated content enrichment, based on an appropri-

ately related content during and after a lecture.

Next to the marking of lecture material using but-

tons, the system supports the attachment of notes to a

selected slide. Notes can be used to enrich a marking

or as simple remarks. InterLect allows an extension of

a note’s text with references to other slides and tags.

InterLect-LectureContentInterface

273

References are represented as unique identiﬁer of a

slide with a leading ”@”. Hashtags are signed with a

”#” symbol as used in systems such as Twitter (Wil-

iams et al., 2006).

The linking of content using references allows the

listener to associate different slides in a non-typiﬁed

way. As described in section 2, linking enables a

consumer of information to relate different pieces

of information. These relations depend on the lis-

tener’s perception and are independent to the sequen-

tial structure of a lecture. The process of linking lec-

ture slides builds an overall non-typiﬁed net of in-

formation and visualizes individual structures of rel-

evance for a listener. Observing these structures of

relevance enables us to examine if links, deﬁned by

listeners, can be used to enhance sequential lecture

material to networks based on relevance.

Hashtags provide the ability to add a semantic

keyword to a running text and performed interactions.

Platforms like Twitter use these keywords to relate

posts to a topic. In the context of our system, a

straight forward use of keywords mark speciﬁc lec-

ture content with a topic. In combination with a for-

mer deﬁned non-typed reference a keyword may be

interpreted as type of link between slides. Doing so,

keywords enhance a non-typed link to a semantic as-

sociation. Based on our didactic analysis described

in 2 our system assists to examine if lecture material

can be enhanced to semantic networks, such as topic

maps (Marius et al., 2008). By observing interactions

of students we identify ways to attach semantic struc-

tures to linear slide shows. These structures may en-

able an autonomous assistance in learning based on

a listener’s interactions. First ideas are described in

(Nicolay, 2014).

InterLect proposes commonly used tags of other

members in the audience. This mechanism supports

a uniﬁcation of used keywords for similar purposes.

Together with the expressed conﬂicts described in ﬁg-

ure 3, uniﬁed keywords in notes may be interpreted as

topics for conﬂicts. Relating topics and conﬂicts en-

ables an export of discussion bubbles as described in

(Vetterick et al., 2014, p.159). A further aim of In-

terLect is to connect listeners after a lecture having

the same type of conﬂict at the same speciﬁc lecture

information.

The third functionality is bookmarking. Book-

marking supports individual revision of the sequential

structure of a talk through punctuation. Bookmarks

either are deﬁned by the audience or the presenter.

Deﬁned by the presenter, they assist in anticipating

the presenters intended structure similar to the pro-

cess of priming described in (Popova et al., 2014).

Deﬁned by the audience, they serve as tool to high-

light important anchors during a talk. Bookmarks are

represented as a list of links and highlighted as the

user scrolls through content elements. That way, lis-

teners of a talk have a structural awareness and can

easily jump to important lecture content.

Direct communication between individuals in the

audience during the lecture is not in our focus. Fur-

thermore we accept the distraction during a lecture

for the use of InterLect to increase the beneﬁt for stu-

dents and teachers by interacting on the lecture con-

tent. Therefore we are keeping the distraction as min-

imal as possible. Based on our review of didactic is-

sues in a lecture, we use our evaluation tool to get

valuable insights in the students’ reﬂection process

as well as the overall perception of current lectures.

Based on our ﬁndings we aim to enhance the pro-

totype with assistance capabilities for lecturers and

students such as methods of semantic enhancement

of existing slide material, aggregated feedback of the

audience’s reﬂection process, and conﬂict driven con-

tent selection for a dedicated content enrichment.

6 RESULTS AND CONCLUSIONS

Processing the teaching content in lecture scenarios

are not deeply covered yet. InterLect is a prototype

build upon concepts of learning theory described in

2. It serves as analytic tool to examine the behavior

of students during a lecture. We emphasize that In-

terLect aims not to stimulate the set of possible inter-

actions performed by students during a lecture. Our

main focus is to use InterLect to gain insight to ac-

tions requested by students during content processing

and to ﬁnd technical ways supporting these actions.

Therefore, InterLect comprises two main aspects.

First, it helps to intervene in the process of content

consumption. It extents known ways to process con-

tent based on concepts of marking, linking and con-

ﬂicting. Second, it serves as analytic tool. It allows

a free usage of all of these concepts. That way, it en-

ables an empirical degree of freedom while using this

tool, allowing an inductive analysis of generative ac-

tivities arising during content cognition. These analy-

ses are performed by evaluating studies based on user

generated data as well as by attendant user surveys.

7 FUTURE WORK

Having an insight into individual needs and require-

ments during a lecture, we aim to build a tool to en-

hance current lecture slide material with semantic in-

formation, which will cover linking necessary previ-

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274

ous knowledge, for example. Current approaches fo-

cus on using topic maps (Marius et al., 2008) and are

described in (Nicolay, 2014). We plan to enhance lec-

ture material either through manual preparation done

by the presenter of a lecture or automatically through

crowd sourced observations of performed students’

interactions.

Further development goals to assist content pro-

cessing during and after a lecture, using mentioned

enhanced lecture material, are:

• Improve the awareness of the structural overview

of a currently held talk

• Analysis of user needs for assistive content selec-

tion

• Provision of assistive content enrichment based

on content selection and lecture material

• Improvement of content distribution on environ-

mental and mobile visual channels based on ag-

gregated content selection and requirement map-

ping in lecture rooms using mobile devices

• Aggregate feedback related to speciﬁc lecture ma-

terial for the lecturer

• Analysis of user conﬂicts for dedicated post-

processing assistance and to initiate study group-

ing

ACKNOWLEDGEMENTS

This work was supported in part by the German Fed-

eral Ministry of Education and Research (BMBF), the

German Research Foundation (DFG) and the Univer-

sity of Rostock. We gratefully acknowledge the pro-

fessional and motivating atmosphere offered by them

in supporting our study.

REFERENCES

Adobe Systems (2014). PDF-Reader, PDF-Viewer Adobe

Reader XI: http://perma.cc/N5NZ-VCGN.

Biggs, J. (1999). What the student does: teaching for en-

hanced learning. Higher Education Research & De-

velopment, 18(1):57–75.

Dunn, P. K., Richardson, A., Oprescu, F., and McDonald, C.

(2013). Mobile-phone-based classroom response sys-

tems: Students perceptions of engagement and learn-

ing in a large undergraduate course. International

Journal of Mathematical Education in Science and

Technology, 44(8):1160–1174.

Feiten, L., Buehrer, M., Sester, S., and Becker, B.

(2012). Smile-smartphones in lectures-initiating a

smartphone-based audience response system as a stu-

dent project. In CSEDU (1), pages 288–293.

Gal, A. (2011). PDF.js: http://perma.cc/HWN7-UELE.

Hanley, M. (2013). Ball State College Student Cell Phone

Study Summary February 2013: College Student

Smartphone Usage Hits 74%; Tablet Ownership at

30%.

Hannaﬁn, M. J., Hill, J. R., Land, S. M., and Lee, E. (2014).

Student-centered, open learning environments: Re-

search, theory, and practice. In Spector, J. M., Merrill,

M. D., Elen, J., and Bishop, M. J., editors, Handbook

of research on educational communications and tech-

nology, pages 641–651. Springer, New York, Heidel-

berg.

Hickson, I. and WHATWG (2014). w3.org - The canvas

element HTML5: http://perma.cc/3PJX-T8HH.

Jenkins, A. (2007). Technique and technology: Electronic

voting systems in an english literature lecture. Peda-

gogy, 7(3):526–533.

Kay, R. H. and LeSage, A. (2009). Examining the beneﬁts

and challenges of using audience response systems:

A review of the literature. Computers & Education,

53(3):819–827.

Kundisch, D., Sievers, M., Zoyke, A., Herrmann, P., Whit-

taker, M., Beutner, M., Fels, G., and Magenheim, J.

(2012). Designing a web-based application to support

peer instruction for very large groups. International

Conference on Information Systems 2012.

Latour, B. and Woolgar, S. (1986). Laboratory life: The

construction of scientiﬁc facts. Princeton University

Press, Princeton, N.J.

Laurillard, D. (2008). Digital technologies and their role in

achieving our ambitions for education.

Laurillard, D. (2012). Teaching as a Design Science: Build-

ing Pedagogical Patterns for Learning and Technol-

ogy. Routledge, New York.

Lee, H. W., Lim, K. Y., and Grabowski, B. L. (2008).

Generative learning: Principles and implications for

making meaning. In Spector, J. M., Merrill, M. D.,

van Merri

enboer, J. J. G., and Driscoll, M. P., edi-

tors, Handbook of research on educational commu-

nications and technology, pages 111–124. Lawrence

Erlbaum Associates, New York.

Leutner, D. and Schmeck, A. (2014). The generative

drawing principle in multimedia learning. In Mayer,

R. E., editor, The Cambridge handbook of multime-

dia learning, Cambridge Handbooks in Psychology,

pages 433–448. Cambridge University Press, Cam-

bridge.

Lucariello, J. (2015). How do i get my students over their al-

ternative conceptions (misconceptions) for learning?

http://perma.cc/wbk2-dd76.

Marius, L., Moore, G., and Moore, G. (2008). ISO 13250-2:

Topic Maps — Data Model: http://perma.cc/MPT4-

MVLM.

Microsoft (2014). Slide Presentation Software - Microsoft

PowerPoint: http://perma.cc/8ABA-P3Z5: Make your

point.

Mioduser, D. (2015). The pedagogical ecology of technol-

ogy education: An agenda for future research and de-

velopment. In Williams, P. J., Jones, A., and Bunt-

ting, C., editors, The future of technology education,

InterLect-LectureContentInterface

275

Contemporary Issues in Technology Education, pages

77–98. Springer, Singapore.

Nicolay, R. (2014). Semantic Enhancement of Lecture Ma-

terial: arXiv:1412.2901 [cs.CY].

Otto, M. (2011). Bootstrap - The world’s most popu-

lar mobile-ﬁrst and responsive front-end framework:

http://perma.cc/99TT-BFL6.

Popova, A., Kirschner, P. A., and Joiner, R. (2014). Effects

of primer podcasts on stimulating learning from lec-

tures: How do students engage? British Journal of

Educational Technology, 45(2):330–339.

Tobias, S. (2010). Generative learning theory, paradigm

shifts, and constructivism in educational psychology:

A tribute to merl wittrock. Educational Psychologist,

45(1):51–55.

van Merri

enboer, J. J. G. and Kester, L. (2014). The four-

component instructional design model: Multimedia

principles in environments for complex learning. In

Mayer, R. E., editor, The Cambridge handbook of

multimedia learning, Cambridge Handbooks in Psy-

chology, pages 71–96. Cambridge University Press,

Cambridge.

Vetterick, J., Garbe, M., and Cap, C. H. (2013). Tweed-

back: A live feedback system for large audiences. In

5th International Conference on Computer Supported

Education (CSEDU2013).

Vetterick, J., Schwennigcke, B., Langfeld, A., Cap, C. H.,

and Sucharowski, W. (2014). Making Classroom Re-

sponse Systems More Social. In 6th International

Conference on Computer Supported Education, pages

153–161.

Wiliams, E., Glass, N., Dorsey, J., and Biz, S. (2006). Wel-

come to Twitter: http://perma.cc/F2CV-BGZD.

Wittrock, M. C. (1990). Generative processes of compre-

hension. Educational Psychologist, 24:345–376.

Wittrock, M. C. (1992). Generative learning processes of

the brain. Educational Psychologist, 27(4):531–541.

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