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Towards Mapping Competencies through Learning Analytics: Real-Time Competency Assessment for Career Direction through Interactive Simulation

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Title: Towards Mapping Competencies through Learning Analytics: Real-Time Competency Assessment for Career Direction through Interactive Simulation
Language: English
Authors: Khatri, Puja, Raina, Khushboo (ORCID 0000-0003-0390-5545), Wilson, Caroline (ORCID 0000-0002-0213-506X), Kickmeier-Rust, Michael
Source: Assessment & Evaluation in Higher Education. 2020 45(6):875-887.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 13
Publication Date: 2020
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learning Analytics, Competence, Career Planning, Computer Simulation, Learner Engagement, Decision Making, Foreign Countries, Career Choice, Game Based Learning, Educational Games, Program Effectiveness, Graduate Students, Business Administration Education, Young Adults, Student Interests
Geographic Terms: India
DOI: 10.1080/02602938.2019.1689382
ISSN: 0260-2938
Abstract: The selection of career paths and making of academic choices is a difficult and often confusing task for young people. The impact on their lives, however, is enormous as it can determine entire future career possibilities. In India, a general remedy to this stress is that instead of choosing a field of study tailored to individual preferences and strengths, topics are chosen that align with the choices of the students' families or their friends. This can have the effect of entrenching patterns of intergenerational inequity. The aim of this research is to give students greater access to the knowledge capital which will help them make better choices. This is achieved by engaging students in the career planning process, in order to convey information in a likeable and credible way. The COMPCAT (Competency and Career Assessment Tool) game engine combines the use of learning analytics and real time, interactive computer simulations designed to gain insights into the students' engagement in the making of these complex decisions. This paper presents the conceptual architecture of the game and demonstrates its role in enhancing the learning effectiveness of the students.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1265029
Database: ERIC
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  Value: <anid>AN0145085189;eva01sep.20;2020Aug14.07:59;v2.2.500</anid> <title id="AN0145085189-1">Towards mapping competencies through learning analytics: real-time competency assessment for career direction through interactive simulation </title> <p>The selection of career paths and making of academic choices is a difficult and often confusing task for young people. The impact on their lives, however, is enormous as it can determine entire future career possibilities. In India, a general remedy to this stress is that instead of choosing a field of study tailored to individual preferences and strengths, topics are chosen that align with the choices of the students' families or their friends. This can have the effect of entrenching patterns of intergenerational inequity. The aim of this research is to give students greater access to the knowledge capital which will help them make better choices. This is achieved by engaging students in the career planning process, in order to convey information in a likeable and credible way. The COMPCAT (Competency and Career Assessment Tool) game engine combines the use of learning analytics and real time, interactive computer simulations designed to gain insights into the students' engagement in the making of these complex decisions. This paper presents the conceptual architecture of the game and demonstrates its role in enhancing the learning effectiveness of the students.</p> <p>Keywords: Learning analytics; digital educational games; higher education; career competency assessment; student engagement</p> <hd id="AN0145085189-2">Introduction</hd> <p>Technology is having an increasing and substantial influence in all areas of education (Kovanovic [<reflink idref="bib24" id="ref1">24</reflink>]). The growing focus on educational data mining and learning analytics is considered to have high potential for learning efficacy (Adams Becker et al. [<reflink idref="bib1" id="ref2">1</reflink>]). In many countries, new technologies have become mainstream educational tools. In India, there is a clear absence of these tools and techniques to assess the learning, skills and performance outcomes of students. At the same time, the country needs a rapid enhancement in the quality and quantity of graduates who are ready to take on graduate-level jobs that match their skills. Such skilled workers are in short supply, and can command a skill premium (Cain et al. [<reflink idref="bib6" id="ref3">6</reflink>]). The premium is contributing to widening inequality, in that, while India's economy has been growing impressively, there is a persistent trend since 1990 of only the top 20% of the population gaining from this (Jain-Chandra et al. [<reflink idref="bib19" id="ref4">19</reflink>]). It is the highly educated who are benefitting most: Cain et al. ([<reflink idref="bib6" id="ref5">6</reflink>]) estimate that education level explains more than 40% of increasing inequality, whether in rural or urban areas. Inequality in India is intergenerational, particularly in the case of low-skilled and low-paying occupations, in that the children of fathers who are low skilled and low paid are more likely to end up on the same path (Motiram and Singh, [<reflink idref="bib29" id="ref6">29</reflink>]). It follows that if a student's choices are limited by the influence of their parents or friends, this cycle is unlikely to be broken.</p> <p>India has the largest youth population in the world, with 600 million young people under the age of 25 (Trines [<reflink idref="bib36" id="ref7">36</reflink>]). In India, only 6% of the total graduates are employable (Sundar [<reflink idref="bib35" id="ref8">35</reflink>]), far fewer than countries having comparable median income. Dropout rates are high. The major reasons for the relative poor participation and success rates in the Indian higher education system are given as; the absence of a personalised approach to education (based on the capabilities of each student), the non-availability of resources, absence of technological infrastructure, absence of skill based learning and absence of real-time modules (Kumar [<reflink idref="bib26" id="ref9">26</reflink>]). At the same time some of the problems for students begin with the poor selection choices of subjects to study. Instead of critically engaging with the options, students end up taking courses irrelevant to their ambitions, only to regret it later (Kumar [<reflink idref="bib25" id="ref10">25</reflink>]).</p> <p>There is a need for the students to take decisions for their subjects and career as per their own latent competencies instead of parental pressure and following traditional career paths unthinkingly. One possible solution to poor decision-making could be to have students take part in a learning analytics gaming simulation designed to gauge their core interests.</p> <p>The present study attempts to analyse this issue. We propose an innovative approach to convey the necessary information and attitudes for an appropriate selection of career opportunities. This approach, termed COMPCAT (Competency and Career Assessment Tool) is based on the strength of game-based learning in combination with learning analytics. The fundamental idea is to use an appealing game plot to engage the users, and to use learning analytics as a means to analyse activities and subject preference in order to support adequate and individualised career counseling. This study is the first one to attempt such assessment of intrinsic interests of students enrolled in higher education institutions in India. It will be beneficial to regulatory bodies, authorities, academics, students, parents and researchers. It also has value to countries beyond India which are also still at the development stage in the use of learning analytics in a career counselling environment. A study by Cuff ([<reflink idref="bib7" id="ref11">7</reflink>]) on UK students found they tended to choose their subjects on the basis of the difficulty level of the subject, advice received from their parents and friends along with how much they enjoyed it. As a consequence, one in three graduates in the UK has a mismatched set of skills to their jobs once they leave university (Steed [<reflink idref="bib33" id="ref12">33</reflink>]). In another study conducted on the students at a university in the USA, students were found to make subject choices on the basis of advice from coaches or teachers or under a sense of family obligation (Fizer [<reflink idref="bib10" id="ref13">10</reflink>]). The fundamental issue of ignoring intrinsic interest and opting for subjects on other criteria is thus global in nature.</p> <hd id="AN0145085189-3">Literature review</hd> <p>Fredericks, Blumenfeld, and Paris ([<reflink idref="bib11" id="ref14">11</reflink>]) define student engagement as a multidimensional construct made up of behavioral, emotional and cognitive elements. An engaged student puts impetus into learning (Burrows, [<reflink idref="bib5" id="ref15">5</reflink>]). Engagement needs to be active to be effective (Appleton et al. [<reflink idref="bib2" id="ref16">2</reflink>]) and learning analytics is a means to support this active learning. Siemens et al. ([<reflink idref="bib32" id="ref17">32</reflink>], p. 4), define learning analytics as, 'the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimising learning and the environments in which it occurs'. According to Adams Becker et al. ([<reflink idref="bib1" id="ref18">1</reflink>]), learning analytics provides a framework to make requisite improvements in learning performance on the basis of the learning history of students. The research pertaining to learning analytics is in a very nascent stage in India, but its potential impact on educational practices is huge (Ellis [<reflink idref="bib9" id="ref19">9</reflink>]). The concept has gained popularity in commercial, academic and political domains. According to Piety, Hickey, and Bishop ([<reflink idref="bib31" id="ref20">31</reflink>]), learning analytics has been described as a part of the field of educational data science which is rapidly emerging and comprises business intelligence (which is creating its space in higher education), educational data mining, web analytics and machine learning.</p> <hd id="AN0145085189-4">Serious games</hd> <p>Over the past decade, game-based learning has entered all educational areas (Dörner et al. [<reflink idref="bib8" id="ref21">8</reflink>]) and meta-reviews have revealed their beneficial effects (e.g. Wouters and van Oostendorp [<reflink idref="bib39" id="ref22">39</reflink>]). A particular strength of serious games is that they motivate and engage students actively. Student engagement is signified by the involvement and effort of students and a change in observable behavior (Yin and Wang [<reflink idref="bib40" id="ref23">40</reflink>]). Indicators of student engagement are academic achievements, belongingness towards the institution, course clarity, attendance, grades, value for learning and assignment completion (Burrows [<reflink idref="bib5" id="ref24">5</reflink>]). Fredericks, Blumenfeld, and Paris ([<reflink idref="bib11" id="ref25">11</reflink>]) identify three dimensions of student engagement: cognitive, emotional/affective and behavioral. Game plots can transport these dimensions into concrete instruction/learning scenarios. These game plots can be personalised to enhance the relevance of the content. There are a variety of techniques which can be used to achieve this, such as cognitive feedback, motivational feedback, meta-cognitive feedback, progression hints and knowledge-based hints (Peirce, Conlan, and Wade 2008). Simulations make use of the applications of interactive dynamic media in order to support users at every step while making choices (Holzinger, Kickmeier-Rust, and Albert [<reflink idref="bib17" id="ref26">17</reflink>]). The mode of the representation of these simulations is very important as it has significant impact on the learning process and performance of the user.</p> <hd id="AN0145085189-5">COMPCAT (Competency and Career Assessment Tool)</hd> <p>From an educational perspective, computer games offer a promising approach to make learning more engaging, satisfying, and probably more effective. COMPCAT established a novel approach to engage students and, at the same time, accompany career planning activities with learning analytics features.</p> <p>The conceptual design of COMPCAT has made use of the methodology adopted in the EC-project ELEKTRA (Enhanced Learning Experience and Knowledge Transfer) as elucidated by Linek et al. ([<reflink idref="bib27" id="ref27">27</reflink>]). The same authors suggested that the methodology used can be taken as a base for other game-based learning modules as well as serious games. Their suggested methodology develops a framework for establishing the structure, establishing interdisciplinary cooperation, and supporting various interrelated subsystems and growth cycles which facilitate the improvements and development of educational games design continuously. The methodology of the ELEKTRA project involves macroadaptivity, microadaptivity, metacognition and motivation. Macroadaptivity refers to the instructional design and managing the available learning situations; microadaptivity refers to the awareness about the skills of the learner and a set of pedagogical rules; metacognition refers to one's knowledge about one's own intellect or cognition; and motivation pertains to various approaches meant for learning and enjoyment.</p> <p>COMPCAT provides a platform where learning and entertainment go hand in hand. The basic idea behind this version of the game at this stage is to make students pursuing higher educational courses at MBA-level aware about the subjects that they are going to study in subsequent semesters and to gauge their interest in a particular subject. The game represents a planet wherein the students would be taken on a space trip and allowed to spend their vacation with a few friendly aliens. Each alien introduces the student to an area of management and its relevance and contribution to sustain life on that planet. The major subjects of management, along with their related streams, are introduced to the students: marketing, human resource management, finance, international business, and information technology. The student is initially allowed to stay with all possible selected aliens and then can in the second phase makes a choice to stay with their preferred aliens (signifying an area of interest) (Figure 1).</p> <p>PHOTO (COLOR): Figure 1. Screenshot of the game. Note: although colour figures presented, they are to be reproduced black and white.</p> <p>The entire scenario is based on a previously developed game named '<emph>Feon's Quest</emph>' (Kickmeier-Rust [<reflink idref="bib20" id="ref28">20</reflink>]). This game has been developed in the context of the European 80Days project and demonstrates adaptive game balancing features on micro as well macro levels (Kickmeier-Rust [<reflink idref="bib21" id="ref29">21</reflink>]). Assessments can be made on the basis of: (i) the frequency of visit to a preferred alien, (ii) time spent with that alien, and (iii) the ability to handle and help that alien. The interaction with the friendly alien companion is designed to provide concrete information about career opportunities and to help students identify their personal preferences and options.</p> <p>Technically, the game is based on the unity game engine and applies a modular conceptual architecture. The approach allows games deployment for the most common technical platforms. To provide the necessary learning analytics features, the game is connected to the open Lea's Box platform (<ulink href="http://eightydays.cognitive-science.eu">http://eightydays.cognitive-science.eu</ulink>). This platform allows connecting data sources (e.g. the game) to an open analytics engine (Kickmeier-Rust and Albert [<reflink idref="bib22" id="ref30">22</reflink>]), which in turn can inform the game through a web service connection about concrete adaptations and recommendations to the students. The online platform allows students, as well as teachers/consultants, to access statistics and analyses of a particular student and their interaction with the game.</p> <hd id="AN0145085189-6">Focus group</hd> <p>Before the research was conducted, three subject experts and three students from an MBA management programme were invited to discuss the detailed design of COMPCAT in a focus group. The focus group lasted 2 hours and was moderated by the first author of this paper. A detailed simulation scenario with the module specifications along with some intermediate snapshots of the game was shown to the group to assess their reactions about the tool. Reactions of the focus group members informed the study team's thinking about the capacity of the tool to increase participant understanding of the concepts of management and the capacity of the tool to serve as a significant catalyst in improvising the decision making of the students.</p> <hd id="AN0145085189-7">Research objectives</hd> <p>The aim of the study is to analyse the effect of the learning analytical tool on the learning effectiveness of the students. It is then to suggest recommendations about the impact of the learning analytical tool on the enhancement of the learning effectiveness of the students. The study was conducted in the year 2017–2018.</p> <p>The hypotheses are that for the students taking part:</p> <p></p> <ulist> <item> <bold> H1: </bold> There exists a positive relationship between the student engagement and learning effectiveness of the students.</item> <p></p> <item> <bold> H2: </bold> There exists a positive relationship between the learning analytical tool and learning effectiveness of the students.</item> <p></p> <item> <bold> H03: </bold> The learning analytical tool does not mediate the relationship between student engagement and learning effectiveness.</item> </ulist> <hd id="AN0145085189-8">Methodology</hd> <p>A cross-sectional survey-based design was adopted to conduct the study. The sample for the study was drawn from a list of institutions held by AICTE (All India Council for Technical Education). Out of the list of 237 institutes, only institutes which offered management courses at the post-graduate level were considered. Of these institutes, five were selected to be considered for the study using the fish bowl simple random sampling technique (performed in front of members of the focus group which had informed the study).</p> <p>The questionnaire had four sections. The first carried questions about the demographic profiles of the respondents. The remaining three carried questions regarding perceptions about the level of student engagement (SE), level of learning effectiveness (LE) and the learning analytical tool (LAT). Based on the comments of the focus group experts and extensive literature review, it was hypothesised that the impact of SE on LE would be mediated by LAT. A mediator is a variable which occupies a position between the independent variable and the dependent variable, and mediation analysis enables investigation of the relative relationship (Hoyle and Robinson [<reflink idref="bib18" id="ref31">18</reflink>]).</p> <hd id="AN0145085189-9">Construct measurements</hd> <p>All items are measured by 7-point Likert scale where '1' represents strongly disagree and '7' represents strongly agree. Scores greater than the average indicate higher level of agreement with the items.</p> <hd id="AN0145085189-10">Student engagement (SE)</hd> <p>The standardised questionnaire devised by Fredericks, Blumenfeld, and Paris ([<reflink idref="bib11" id="ref32">11</reflink>]) was taken to measure this. The indicators for SE in the model are: cognitive engagement (CE), emotional engagement (EE) and behavioural engagement (BE) and the items include 'paying attention in class', 'following instructions at the institution', 'completion of assignments', etc.</p> <hd id="AN0145085189-11">Learning analytical tool (LAT)</hd> <p>A self-constructed questionnaire was taken for the study. The dimensions of LAT in the model are: customised learning environment (CLE), step-wise approach (SWA) and student involvement (SI) and the items comprise 'customisation according to learning behaviour', 'conducive game environment', 'goal oriented', 'step by step approach', etc.</p> <hd id="AN0145085189-12">Learning effectiveness (LE)</hd> <p>A self-constructed questionnaire was taken for the study. The dimensions of LE in the model are: student learning ability (SLA) and student inquisitiveness (SIQ). The items corresponding to these indicators are 'able to reciprocate what is being taught', 'appreciated for being a learner', 'knowledge retention', 'proactive towards academic assignments', etc.</p> <hd id="AN0145085189-13">Participants</hd> <p>Students of 2-year MBA management courses studying in their first year of the programme were administered the questionnaires. First year students were selected (aged between 21 and 24 years) so as to help them take a calculated decision towards elective selection that is scheduled to happen during the second year in their course. The measured change sought was to understand the impact of the learning analytical tool in enhancing the learning effectiveness and the level of engagement. In total, 250 questionnaires were distributed and 161 completed questionnaires were taken for analysis. Out of 161 respondents 54% were males and 45.9% were females, 88% belonged to 20–22 years of age category and 11.8% were from 22 to 24 years of age category.</p> <hd id="AN0145085189-14">Factor analysis</hd> <p>While all three question sets were heavily informed by literature review, only student engagement was identified to have a readily suitable standardised questionnaire. The questions to frame the remaining two constructs (LE and LAT) were validated by applying both exploratory factor analysis and confirmatory factor analysis techniques (see Khatri and Raina, forthcoming). All analysis was conducted using PLS SEM 3.0 SmartPLS.</p> <hd id="AN0145085189-15">Results</hd> <p></p> <hd id="AN0145085189-16">Reliability and validity of the model</hd> <p>Reliability (Table 1) is considered satisfactory when Cronbach <emph>α</emph> and composite reliability are greater than 0.7 (Bagozzi and Yi [<reflink idref="bib3" id="ref33">3</reflink>]; Hair et al. [<reflink idref="bib13" id="ref34">13</reflink>]). Heterotrait-monotrait (HTMT) ratio (Table 2) is a robust technique to assess discriminant validity of latent variables (Henseler, Ringle, and Sarstedt [<reflink idref="bib16" id="ref35">16</reflink>]) and is an estimate of what the true correlation (disattenuated correlation) between two constructs would be, if they were perfectly measured (i.e. if they were perfectly reliable). A disattenuated correlation between two constructs close to one indicates a lack of discriminant validity. Ullman and Bentler ([<reflink idref="bib37" id="ref36">37</reflink>]) suggest that all parameters (dimensions and constructs) are used. Figure 2 contains a visual representation of the discriminant validity of the latent variables.</p> <p>PHOTO (COLOR): Figure 2. Structural model.</p> <p>Table 1. Reliability of the constructs.</p> <p> <ephtml> <table><thead><tr><td>Construct</td><td>Cronbach α</td><td>Composite reliability</td><td>Type of construct</td><td>AVE (average variance extracted)</td></tr></thead><tbody valign="top"><tr><td>Student Engagement</td><td char=".">0.86</td><td char=".">0.892</td><td>Reflective-reflective</td><td>0.510</td></tr><tr><td>Learning Analytical Tool</td><td char=".">0.786</td><td char=".">0.849</td><td>Reflective-formative</td><td>Content validity was established and VIF values were less than 5</td></tr><tr><td>Learning Effectiveness</td><td char=".">0.777</td><td char=".">0.849</td><td>Reflective-reflective</td><td>0.529</td></tr></tbody></table> </ephtml> </p> <p>Table 2. Discriminant validity-Heterotrait-Monotrait ratio (HTMT).</p> <p> <ephtml> <table><thead><tr><td>Constructs</td><td>Dimensions and Constructs</td><td>Behavioural engagement</td><td>Cognitive engagement</td><td>Customised learning environment</td><td>Emotional engagement</td><td>Student involvement</td><td>Learning analytical tool</td><td>Learning effectiveness</td><td>Student engagement</td><td>Student inquisitive-ness</td><td>Student learning ability</td><td>Step wise approach</td></tr></thead><tbody valign="top"><tr><td>SE</td><td>Behavioural Engagement</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>SE</td><td>Cognitive Engagement</td><td>0.393</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>LAT</td><td>Customised Learning Environment</td><td>0.715</td><td>0.370</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>SE</td><td>Emotional Engagement</td><td>0.648</td><td>0.371</td><td>0.714</td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>LAT</td><td>Student Involvement</td><td>0.614</td><td>0.493</td><td>0.644</td><td>0.661</td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>LAT</td><td>Learning Analytical Tool</td><td>0.806</td><td>0.531</td><td>1.023</td><td>0.767</td><td>1.100</td><td /><td /><td /><td /><td /><td /></tr><tr><td>LE</td><td>Learning Effectiveness</td><td>0.751</td><td>0.560</td><td>0.641</td><td>0.581</td><td>0.689</td><td>0.890</td><td /><td /><td /><td /><td /></tr><tr><td>SE</td><td>Student Engagement</td><td>0.932</td><td>0.722</td><td>0.782</td><td>1.020</td><td>0.765</td><td>0.901</td><td>0.781</td><td /><td /><td /><td /></tr><tr><td>LE</td><td>Student Inquisitiveness</td><td>0.607</td><td>0.566</td><td>0.485</td><td>0.531</td><td>0.519</td><td>0.773</td><td>1.217</td><td>0.711</td><td /><td /><td /></tr><tr><td>LE</td><td>Student Learning Ability</td><td>0.835</td><td>0.453</td><td>0.761</td><td>0.553</td><td>0.822</td><td>0.908</td><td>1.166</td><td>0.749</td><td>0.773</td><td /><td /></tr><tr><td>LAT</td><td>Step Wise Approach</td><td>0.646</td><td>0.445</td><td>0.521</td><td>0.512</td><td>0.602</td><td>1.007</td><td>0.851</td><td>0.666</td><td>0.883</td><td>0.653</td><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0145085189-17">Structural model</hd> <p>In SmartPLS, the relationships between constructs can be determined by examining their path coefficients and related <emph>t</emph>-statistics via the bootstrapping procedure (Wong [<reflink idref="bib38" id="ref37">38</reflink>]).The strength of connection (path coefficient) indicates the response of the dependent variable to a unit change in an explanatory variable; under the condition that all other variables in the model are held constant (Bollen [<reflink idref="bib4" id="ref38">4</reflink>]). In a structural equation model, the path coefficients are similar to correlation or regression coefficients (McIntosh and Gonzalez-Lima [<reflink idref="bib28" id="ref39">28</reflink>]). An interpretation of the results of a path model involves testing the significance of all relationships in the structural model by assessing <emph>t</emph>-statistics (calculated by dividing the original sample from its corresponding standard deviation value), <emph>p</emph>-values and bootstrap confidence intervals. In the present work, 5% (<emph>p</emph> < 0.05) has been chosen as the level of significance for analysis of results. Using a two-tailed <emph>t</emph>-test, this 5% significance level requires the t-statistic to be larger than 1.96 (Wong [<reflink idref="bib38" id="ref40">38</reflink>]). Table 3 shows the <emph>t</emph>-statistics and <emph>p</emph>-values across each relationship and indicates the acceptance or rejection of the null hypothesis.</p> <p>Table 3. Significance of the path coefficients.</p> <p> <ephtml> <table><thead><tr><td /><td>Original sample (O)</td><td>Sample mean (M)</td><td>Standard deviation (STDEV)</td><td>T statistics (|O/STDEV|)</td><td><italic>P</italic></td></tr></thead><tbody valign="top"><tr><td>Learning Analytical Tool → Learning Effectiveness</td><td>0.505</td><td char=".">0.498</td><td>0.084</td><td>6</td><td char=".">0.000</td></tr><tr><td>Student Engagement → Learning Analytical Tool</td><td>0.743</td><td char=".">0.739</td><td>0.041</td><td>18.123</td><td char=".">0.000</td></tr><tr><td>Student Engagement → Learning Effectiveness</td><td>0.263</td><td char=".">0.271</td><td>0.084</td><td>3.149</td><td char=".">0.002</td></tr></tbody></table> </ephtml> </p> <p>The relationship between student engagement (SE) and learning effectiveness (LE) was significant (<emph>p</emph> = 0.002), with a <emph>t</emph>-statistic of 3.149 (greater than 1.96) and β (original sample) equal to 0.263. This indicates the acceptance of alternate hypothesis that SE is positively related to LE. Therefore, H1 was accepted. Similarly, the relationship between learning analytical tool (LAT) and LE is found to be significant (<emph>p</emph> < 0.001) with a <emph>t</emph>-statistic of six greater than 1.96) and β original sample) equal to 0.505. This indicates the acceptance of alternate hypothesis that LAT is positively related to LE. Hence, H2 is accepted.</p> <p>Before testing H<subs>0</subs>3, the model containing all three constructs needs to be evaluated. The <emph>R</emph><sups>2</sups> value is the most common measure for doing this. The value of <emph>R</emph><sups>2</sups> (Table 4) lies between 0 to1 and a higher value represents greater predictive accuracy (Hair, Ringle, and Sarstedt [<reflink idref="bib14" id="ref41">14</reflink>]). <emph>F</emph>-square effect size was analysed as it is the change in the <emph>R</emph><sups>2</sups> value on the omission of a particular independent variable from the model (Table 5).</p> <p>Table 4. <emph>R</emph> square of the structural model.</p> <p> <ephtml> <table><tbody valign="top"><tr><td><italic>R</italic><sup>2</sup></td><td>0.52</td></tr></tbody></table> </ephtml> </p> <p>Table 5. <emph>F</emph> square effect size.</p> <p> <ephtml> <table><thead><tr><td /><td>Learning analytical tool</td><td>Learning effectiveness</td><td>Student engagement</td></tr></thead><tbody valign="top"><tr><td>Learning Analytical Tool</td><td /><td char=".">0.239</td><td /></tr><tr><td>Learning Effectiveness</td><td /><td /><td /></tr><tr><td>Student Engagement</td><td>1.23</td><td char=".">0.065</td><td /></tr></tbody></table> </ephtml> </p> <p>In addition to evaluating the magnitude of <emph>R</emph><sups>2</sups> Stone-Geisser's <emph>Q</emph><sups>2</sups> value should be examined (Geisser [<reflink idref="bib12" id="ref42">12</reflink>]; Stone [<reflink idref="bib34" id="ref43">34</reflink>]). <emph>Q</emph><sups>2</sups> in Table 6 represents the predictive relevance of the model. The model has predictive relevance for a dependent variable if the <emph>Q</emph><sups>2</sups> value for this dependent variable is greater than zero.</p> <p>Table 6. <emph>Q</emph>-square construct cross-validated redundancy.</p> <p> <ephtml> <table><thead><tr><td /><td>SSO</td><td>SSE</td><td><italic>Q</italic><sup>2</sup> (=1-SSE/SSO)</td></tr></thead><tbody valign="top"><tr><td>Learning Analytical Tool</td><td>161</td><td char=".">75.095</td><td>0.534</td></tr><tr><td>Learning Effectiveness</td><td>161</td><td char=".">79.92</td><td>0.504</td></tr><tr><td>Student Engagement</td><td>161</td><td char=".">161</td><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0145085189-18">Mediation analysis</hd> <p>Having evaluated the model as a whole, mediation analysis was conducted. Hayes' (2009) method was implemented based on the argument that two direct relationships can't result in an indirect relationship. The process entails the estimation of the total and direct effect of predictor on criterion, as well as the indirect effect of the independent variable on the dependent variable through mediator.</p> <hd id="AN0145085189-19">Direct effect</hd> <p>First, the unmediated path between SE and LE was analysed (Figure 3) and it was observed that SE to LE had a significant <emph>β</emph> of 0.638 and produced an <emph>R</emph><sups>2</sups> of 0.408 for LE (Table 7).</p> <p>PHOTO (COLOR): Figure 3. Direct effect of SE on LE.</p> <p>Table 7. Direct effect of SE on LE.</p> <p> <ephtml> <table><thead><tr><td /><td>Original sample (<italic>O</italic>)</td><td>Sample mean (<italic>M</italic>)</td><td>Standard deviation (STDEV)</td><td><italic>T</italic> statistics (|O/STDEV|)</td><td><italic>P</italic></td></tr></thead><tbody valign="top"><tr><td>Student Engagement → Learning Effectiveness</td><td char=".">0.638</td><td char=".">0.641</td><td>0.051</td><td>12.581</td><td char=".">0.000</td></tr></tbody></table> </ephtml> </p> <hd id="AN0145085189-20">Indirect effects</hd> <p>When the mediation relationship with LAT was added, the new paths were significant (SE to LAT had a <emph>β</emph> of 0.743 and LAT to LE had a <emph>β</emph> of 0.505). Importantly, the direct path between SE and LE was still significant with a <emph>β</emph> of 0.263 (Tables 8–10 and Figure 4). These results validated our model by providing strong evidence that LAT acts as a partial mediator and that predicting only a direct relationship between SE and LE is suboptimal. Therefore, H<subs>0</subs>3 was rejected.</p> <p>PHOTO (COLOR): Figure 4. Mediation model corresponding to the model four given by Hayes ([<reflink idref="bib15" id="ref44">15</reflink>]).</p> <p>Table 8. Indirect effect student engagement*learning effectiveness.</p> <p> <ephtml> <table><thead><tr><td /><td>Learning analytical tool</td><td>Learning effectiveness</td><td>Student engagement</td></tr></thead><tbody valign="top"><tr><td>Learning Analytical Tool</td><td /><td /><td /></tr><tr><td>Learning Effectiveness</td><td /><td /><td /></tr><tr><td>Student Engagement</td><td /><td char=".">0.375</td><td /></tr></tbody></table> </ephtml> </p> <p>Table 9. Significance of the indirect effect.</p> <p> <ephtml> <table><thead><tr><td /><td>Original sample (<italic>O</italic>)</td><td>Sample mean (<italic>M</italic>)</td><td>Standard deviation (STDEV)</td><td><italic>T</italic> statistics (|O/STDEV|)</td><td><italic>P</italic></td></tr></thead><tbody valign="top"><tr><td>Student Engagement → Learning Effectivenes<bold>s</bold></td><td><bold>0.375</bold></td><td char=".">0.374</td><td>0.069</td><td>5.44</td><td char=".">0.000</td></tr></tbody></table> </ephtml> </p> <p>Table 10. Direct and indirect effects with significance.</p> <p> <ephtml> <table><thead><tr><td /><td>Original sample (<italic>O</italic>)</td><td>Sample mean (<italic>M</italic>)</td><td>Standard deviation (STDEV)</td><td><italic>T</italic> statistics (|O/STDEV|)</td><td><italic>P</italic></td></tr></thead><tbody valign="top"><tr><td>Learning Analytical Tool → Learning Effectiveness</td><td>0.505</td><td char=".">0.504</td><td>0.09</td><td>5.591</td><td char=".">0.000</td></tr><tr><td>Student Engagement → Learning Analytical Tool</td><td>0.743</td><td char=".">0.743</td><td>0.041</td><td>18.152</td><td char=".">0.000</td></tr><tr><td>Student Engagement → Learning Effectiveness</td><td>0.263</td><td char=".">0.267</td><td>0.089</td><td>2.96</td><td char=".">0.003</td></tr></tbody></table> </ephtml> </p> <hd id="AN0145085189-21">Discussion and conclusion</hd> <p>Indian graduates have low employability rates and one of the key reasons for this is career choices which are mismatched to their competences. The intrinsic competency and interests of the students are neglected in favour of careers that align with the choices of the students' families or friends. This can have the effect of entrenching intergenerational inequality. There is urgent need of a tool which can provide a pedagogical approach to enhance the knowledge capital of students, in order to support choices more aligned to competencies. Digital educational games combine learning and enjoyment to offer a potential solution. The COMPCAT game engine, aligned with the use of learning analytics, is able to map the competency of the students to the career matching their personal profiles.</p> <p>The findings of the study suggest that learning analytics is a potentially powerful tool for enhancing the learning effectiveness of the students in the context discussed in this paper. A partial mediation effect of the learning analytical tool was observed between the relationship of student engagement and learning effectiveness. Thus, the learning analytic tool enhances learning effectiveness. COMPCAT will serve as a catalyst in enhancing the knowledge capital of the students so that they can choose their next subjects in alignment with their intrinsic interests. The applications of the module can be replicated for second year students wherein their intrinsic interests can be mapped to facilitate their decision making to choose an appropriate career. The potential for the tool is that the career decision making of students generally, could become much better once they are aware of their intrinsic competence.</p> <p>The dimensions to measure learning analytical tool and learning effectiveness are another significant contribution of the study. The comprehensive list of factors enhances the understanding of the concepts and enables future researchers to explore other avenues using these indicators.</p> <p>The present study is highly pertinent for higher education institutions in India and can be extended to school students also. It also offers insights for countries globally. Although the use of gaming and learning analytics may be more developed in higher education settings beyond India, it is still the case that students globally have a mismatched set of skills to their jobs once they leave university. A tool to address the fundamental issue of sub-optimal subject choices has the potential to boost social mobility by helping guide students towards careers which match their intrinsic competence, and which are successful and rewarding.</p> <hd id="AN0145085189-22">Disclosure statement</hd> <p>No potential conflict of interest was reported by the authors.</p> <ref id="AN0145085189-23"> <title> References </title> <blist> <bibl id="bib1" idref="ref2" type="bt">1</bibl> <bibtext> Adams Becker, S., M. Cummins, A. Davis, A. Freeman, C. Hall Giesinger, and V. Ananthanarayanan. 2017. " NMC Horizon Report: 2017 Higher Education Edition." The New Media Consortium. Austin. Retrieved from https://<ulink href="http://www.sconul.ac.uk/sites/default/files/documents/2017-nmc-horizon-report-he-EN.pdf">www.sconul.ac.uk/sites/default/files/documents/2017-nmc-horizon-report-he-EN.pdf</ulink></bibtext> </blist> <blist> <bibl id="bib2" idref="ref16" type="bt">2</bibl> <bibtext> Appleton, J. J., S. L. 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Her industry-academic experience extends to 19 years.She is a keen researcher and has contributed more than 134 research papers and articles published in journals of national and international repute and in proceedings of national and international conferences. She has to her credit edited books in the area of Human Resource Management ,business communication and Personality Development, Mind Management, Management by Optimism, sustainable development in organizations and mind management for management. She is a member of the editorial board of Indraprastha Journal of Management published by USMS. She has also been a member of editorial board of UFocus (A triannual newsletter of GGSIP University). She is currently chairperson skill development and mentoring cell. She is also certified for advanced behavioural testing and analysis by GGSIP University. She has conducted several Management development programmes and Faculty Development programmes.Her areas of consultancy include motivation, stress management, Skill Assessment and Interpersonal sensitivity Analyst, Behaviour Analyst and Trainer with personality assessment being her forte.</p> <p>Khushboo Raina has done her doctoral work in the area of Human resource management and Higher education from Guru Gobind Singh Indrapratha University. She has more than 7 years of research and teaching experience. She has to her credit more than 30 papers published in Journals and Conference Proceedings of National and International repute and also 1 edited book. Her research interests include the areas of Organization Behviour, Information technology, Business communication and Social sciences. Currently, she is an Assistant Professor at Delhi Institute of Advanced Studies (affiliated to GGSIPU), Delhi, India.</p> <p>Caroline Wilson is an Associate Professor and Curriculum Change Lead at Coventry University, embedding issues such as sustainability and inclusivity into the learning environment. Research interests include learning from other disciplines how to invoke positive change.</p> <p>Michael Kickmeier-Rust is a Professor of Psychology at the University of Teacher Education St. Gallen, Switzerland. He holds a PhD in Cognitive Psychology and has a strong technical background as software developer. His research focuses on pedagogical diagnostics, smart embedded competence assessment, serious games and embedded, game-based assessment and training.</p> </aug> <nolink nlid="nl1" bibid="bib24" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib19" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib29" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib36" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib35" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib26" firstref="ref9"></nolink> <nolink nlid="nl7" bibid="bib25" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib33" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib10" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib11" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib32" firstref="ref17"></nolink> <nolink nlid="nl12" bibid="bib31" firstref="ref20"></nolink> <nolink nlid="nl13" bibid="bib39" firstref="ref22"></nolink> <nolink nlid="nl14" bibid="bib40" firstref="ref23"></nolink> <nolink nlid="nl15" bibid="bib17" firstref="ref26"></nolink> <nolink nlid="nl16" bibid="bib27" firstref="ref27"></nolink> <nolink nlid="nl17" bibid="bib20" firstref="ref28"></nolink> <nolink nlid="nl18" bibid="bib21" firstref="ref29"></nolink> <nolink nlid="nl19" bibid="bib22" firstref="ref30"></nolink> <nolink nlid="nl20" bibid="bib18" firstref="ref31"></nolink> <nolink nlid="nl21" bibid="bib13" firstref="ref34"></nolink> <nolink nlid="nl22" bibid="bib16" firstref="ref35"></nolink> <nolink nlid="nl23" bibid="bib37" firstref="ref36"></nolink> <nolink nlid="nl24" bibid="bib38" firstref="ref37"></nolink> <nolink nlid="nl25" bibid="bib28" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib14" firstref="ref41"></nolink> <nolink nlid="nl27" bibid="bib12" firstref="ref42"></nolink> <nolink nlid="nl28" bibid="bib34" firstref="ref43"></nolink> <nolink nlid="nl29" bibid="bib15" firstref="ref44"></nolink>
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  Data: <searchLink fieldCode="AR" term="%22Khatri%2C+Puja%22">Khatri, Puja</searchLink><br /><searchLink fieldCode="AR" term="%22Raina%2C+Khushboo%22">Raina, Khushboo</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-0390-5545">0000-0003-0390-5545</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wilson%2C+Caroline%22">Wilson, Caroline</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-0213-506X">0000-0002-0213-506X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kickmeier-Rust%2C+Michael%22">Kickmeier-Rust, Michael</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Assessment+%26+Evaluation+in+Higher+Education%22"><i>Assessment & Evaluation in Higher Education</i></searchLink>. 2020 45(6):875-887.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2020
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Competence%22">Competence</searchLink><br /><searchLink fieldCode="DE" term="%22Career+Planning%22">Career Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Simulation%22">Computer Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Career+Choice%22">Career Choice</searchLink><br /><searchLink fieldCode="DE" term="%22Game+Based+Learning%22">Game Based Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Games%22">Educational Games</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Business+Administration+Education%22">Business Administration Education</searchLink><br /><searchLink fieldCode="DE" term="%22Young+Adults%22">Young Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Interests%22">Student Interests</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22India%22">India</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/02602938.2019.1689382
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0260-2938
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The selection of career paths and making of academic choices is a difficult and often confusing task for young people. The impact on their lives, however, is enormous as it can determine entire future career possibilities. In India, a general remedy to this stress is that instead of choosing a field of study tailored to individual preferences and strengths, topics are chosen that align with the choices of the students' families or their friends. This can have the effect of entrenching patterns of intergenerational inequity. The aim of this research is to give students greater access to the knowledge capital which will help them make better choices. This is achieved by engaging students in the career planning process, in order to convey information in a likeable and credible way. The COMPCAT (Competency and Career Assessment Tool) game engine combines the use of learning analytics and real time, interactive computer simulations designed to gain insights into the students' engagement in the making of these complex decisions. This paper presents the conceptual architecture of the game and demonstrates its role in enhancing the learning effectiveness of the students.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2020
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1265029
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1265029
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  BibEntity:
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      – Type: doi
        Value: 10.1080/02602938.2019.1689382
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 875
    Subjects:
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Competence
        Type: general
      – SubjectFull: Career Planning
        Type: general
      – SubjectFull: Computer Simulation
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Decision Making
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Career Choice
        Type: general
      – SubjectFull: Game Based Learning
        Type: general
      – SubjectFull: Educational Games
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      – SubjectFull: Program Effectiveness
        Type: general
      – SubjectFull: Graduate Students
        Type: general
      – SubjectFull: Business Administration Education
        Type: general
      – SubjectFull: Young Adults
        Type: general
      – SubjectFull: Student Interests
        Type: general
      – SubjectFull: India
        Type: general
    Titles:
      – TitleFull: Towards Mapping Competencies through Learning Analytics: Real-Time Competency Assessment for Career Direction through Interactive Simulation
        Type: main
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            NameFull: Khatri, Puja
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            NameFull: Raina, Khushboo
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            NameFull: Wilson, Caroline
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            NameFull: Kickmeier-Rust, Michael
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              Type: published
              Y: 2020
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              Value: 0260-2938
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              Value: 45
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              Value: 6
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            – TitleFull: Assessment & Evaluation in Higher Education
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